WO2016188065A1 - 云名片推荐方法及装置 - Google Patents

云名片推荐方法及装置 Download PDF

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Publication number
WO2016188065A1
WO2016188065A1 PCT/CN2015/095333 CN2015095333W WO2016188065A1 WO 2016188065 A1 WO2016188065 A1 WO 2016188065A1 CN 2015095333 W CN2015095333 W CN 2015095333W WO 2016188065 A1 WO2016188065 A1 WO 2016188065A1
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Prior art keywords
avatar
business card
contact
cloud business
similarity
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PCT/CN2015/095333
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English (en)
French (fr)
Inventor
鲍协浩
邱诗定
牛坤
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小米科技有限责任公司
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Application filed by 小米科技有限责任公司 filed Critical 小米科技有限责任公司
Priority to MX2017005371A priority Critical patent/MX2017005371A/es
Priority to JP2017504776A priority patent/JP6343388B2/ja
Priority to KR1020167015099A priority patent/KR20160150635A/ko
Priority to RU2016140113A priority patent/RU2656978C2/ru
Publication of WO2016188065A1 publication Critical patent/WO2016188065A1/zh

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    • G06Q10/00—Administration; Management
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G—PHYSICS
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Definitions

  • the present disclosure relates to the field of computer technologies, and in particular, to a cloud business card recommendation method and apparatus.
  • the smart terminal has become a daily product in people's lives, and some social applications can be installed in the smart terminal, and the user records the contact information of the unmatched contact in the social application.
  • the contact avatar, and the user can upload the contact information and the contact avatar to the server to generate a cloud business card, and open the cloud business card, so that other users can see the contact's cloud business card.
  • Embodiments of the present disclosure provide a cloud business card recommendation method and apparatus.
  • the cloud business card of the contact is recommended to other users, the accuracy of the recommended cloud business card is improved.
  • a cloud business card recommendation method comprising:
  • the cloud business card avatar matches the contact avatar
  • the cloud business card of the first contact is recommended to the second contact.
  • the technical solution provided by the embodiment of the present disclosure may include the following benefits: acquiring a cloud business card of a first contact that matches a contact avatar in a server, and pushing a cloud business card of the first contact to the second contact. Whether the cloud business card in the server matches the contact information stored in the terminal of the second contact is determined according to the avatar, and the accuracy of matching the cloud business card with the contact information when the cloud business card is recommended to the second contact is improved.
  • Comparing the cloud business card avatar with the contact avatar including:
  • the technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: determining whether the cloud business card avatar matches the contact avatar by determining the first similarity.
  • the cloud business card avatar of the same person does not necessarily match the contact avatar. Therefore, when the first similarity reaches the preset similarity, it can be determined that the cloud business card and the contact information are the same person, further improving the recommendation of the cloud business card to the first The accuracy of matching the cloud business card with the contact information when the second contact is used.
  • Comparing the cloud business card avatar with the contact avatar including:
  • the technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: determining whether the cloud business card avatar matches the contact avatar by determining the cloud business card avatar and the face image extracted in the contact avatar.
  • the cloud business card avatar matches the face image of the contact avatar it can be determined that the cloud business card and the contact information are the same person, which improves the speed of judging whether the cloud business card avatar and the contact avatar face image match, and further improves the speed.
  • comparing the cloud business card avatar with the contact avatar further includes:
  • different contacts may select some of the same network pictures as contact avatars or cloud business card avatars.
  • the number of occurrences of the cloud business card avatar or the contact avatar is greater than the preset number of times, it may be determined that the avatar is a plurality of contacts or a cloud business card avatar, instead of a cloud business card or a contact avatar of the same person. Therefore, when the number of occurrences is less than or equal to the preset number of times, determining that the cloud business card avatar matches the contact avatar further improves the accuracy of determining whether the cloud business card avatar matches the contact avatar.
  • comparing the cloud business card avatar with the contact avatar further includes:
  • the technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: calculating a second similarity between the cloud business card avatar and the contact avatar by the cloud business card avatar or the number of contact avatar occurrences, and the second similarity is less than the preset similarity.
  • the cloud business card avatar does not match the contact avatar.
  • Different contacts may select some of the same web images as contact avatars or cloud business avatars.
  • the similarity between the cloud business card avatar and the contact avatar is smaller, and the avatar may be determined as multiple contacts or cloud business card avatars instead of the same person's cloud business card or contact. Human avatar. Therefore, when the second similarity is less than the preset similarity, it is determined that the cloud business card avatar does not match the contact avatar, which further improves the accuracy of determining whether the cloud business card avatar matches the contact avatar.
  • the method further includes:
  • the comparing the cloud business card avatar with the contact avatar including:
  • the technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: by comparing the feature points of the cloud business card avatar and the standard avatar of the contact avatar to determine whether the cloud business card avatar matches the contact avatar, the comparison cloud business card avatar and the contact may be improved.
  • the speed of the human avatar can also improve the accuracy of judging whether the cloud business card avatar matches the contact avatar, and further improves the accuracy of matching the cloud business card with the contact information when recommending the cloud business card to the second contact.
  • the method further includes:
  • Comparing the cloud business card avatar with the face image of the contact avatar including:
  • the technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: by comparing the feature points of the cloud business card avatar and the standard face image of the contact avatar to determine whether the cloud business card avatar matches the contact avatar, the contrast cloud business card avatar may be improved.
  • the speed of the contact avatar can also improve the accuracy of determining whether the cloud business card avatar matches the contact avatar.
  • the accuracy of matching the cloud business card with the contact information when the cloud business card is recommended to the second contact is further improved.
  • a cloud business card recommending apparatus comprising:
  • An acquiring module configured to obtain a cloud business card of the first contact, and contact information stored in the terminal of the second contact, where the cloud business card includes a business card avatar, and the contact information includes a contact avatar;
  • a comparison module configured to compare the cloud business card avatar with the contact avatar
  • a recommendation module configured to: when the cloud business card avatar matches the contact avatar, recommend the cloud business card of the first contact to the second contact.
  • Comparison module including:
  • a first calculation submodule configured to calculate a first similarity between the cloud business card avatar and the contact avatar
  • the first determining submodule is configured to determine that the cloud business card avatar matches the contact avatar when the first similarity reaches a preset similarity.
  • Comparison module including:
  • Extracting a sub-module configured to extract a face image from the cloud business card avatar and the contact avatar respectively;
  • a comparison submodule configured to compare the cloud business card avatar with the face image of the contact avatar
  • the second determining submodule is configured to determine that the cloud business card avatar matches the contact avatar when the cloud business card avatar and the contact avatar face image match.
  • the comparison module also includes:
  • a third determining submodule configured to: when the first similarity reaches a preset similarity, determine that the cloud business card avatar or the contact avatar is in all cloud business card avatars and contact avatars stored by the server The number of occurrences;
  • a fourth determining submodule configured to determine that the cloud business card avatar matches the contact avatar when the number of occurrences is less than or equal to a preset number of times.
  • the comparison module also includes:
  • a second calculation sub-module configured to calculate, according to the number of occurrences and the first similarity, a second similarity between the cloud business card avatar and the contact avatar, when the number of occurrences is greater than a preset number of times, The higher the number of occurrences, the smaller the second similarity is relative to the first similarity;
  • a determining submodule configured to determine whether the second similarity reaches the preset similarity
  • the fifth determining submodule is configured to determine that the cloud business card avatar does not match the contact avatar when the second similarity does not reach the preset similarity.
  • the device also includes:
  • a first processing module configured to normalize the cloud business card avatar and the contact avatar to obtain the cloud a business card avatar and a standard avatar of the contact avatar;
  • a first extraction module configured to extract feature points according to a preset rule for the standard avatar
  • the comparison module includes:
  • a comparison submodule configured to compare feature points of the cloud business card avatar and a standard avatar of the contact avatar
  • the sixth determining submodule is configured to determine, according to the comparison result of the feature points, whether the cloud business card avatar matches the contact avatar.
  • the device also includes:
  • a second processing module configured to perform normalization processing on the cloud business card avatar and the face image of the contact avatar to obtain a standard face image of the cloud business card avatar and the contact avatar;
  • a second extraction module configured to extract feature points according to a preset rule on the standard face image
  • the comparison sub-module is further configured to compare feature points of the cloud business card avatar and the standard face image of the contact avatar; and determine the cloud business card avatar and the contact avatar according to the comparison result of the feature points Whether the face image matches.
  • a cloud business card recommending apparatus including:
  • a memory for storing processor executable instructions
  • processor is configured to:
  • the cloud business card avatar matches the contact avatar
  • the cloud business card of the first contact is recommended to the second contact.
  • FIG. 1 is a flowchart of a cloud business card recommendation method according to an exemplary embodiment.
  • FIG. 2 is a detailed flowchart of a cloud business card recommendation method according to an exemplary embodiment.
  • FIG. 3 is a detailed flowchart of a cloud business card recommendation method according to an exemplary embodiment.
  • FIG. 4 is a detailed flowchart of a cloud business card recommendation method according to an exemplary embodiment.
  • FIG. 5 is a detailed flowchart of a cloud business card recommendation method according to an exemplary embodiment.
  • FIG. 6 is a block diagram of a cloud business card recommending apparatus according to an exemplary embodiment.
  • FIG. 7 is a block diagram of a comparison module in a cloud business card recommendation device, according to an exemplary embodiment.
  • FIG. 8 is a block diagram of a comparison module in a cloud business card recommendation device, according to an exemplary embodiment.
  • FIG. 9 is a block diagram of a comparison module in a cloud business card recommendation device, according to an exemplary embodiment.
  • FIG. 10 is a block diagram of a comparison module in a cloud business card recommendation device, according to an exemplary embodiment.
  • FIG. 11 is a block diagram of a cloud business card recommending apparatus according to an exemplary embodiment.
  • FIG. 12 is a block diagram of a cloud business card recommending apparatus according to an exemplary embodiment.
  • FIG. 13 is a block diagram of a cloud business card recommending apparatus according to an exemplary embodiment.
  • FIG. 14 is a block diagram of a cloud business card recommending apparatus according to an exemplary embodiment.
  • smart terminals have become everyday items in people's lives, and some social applications can be installed in smart terminals, and users record contact information and contacts of different contacts in social applications.
  • the person avatar, and the user can upload the contact information and the contact avatar to the server to generate a cloud business card, and open the cloud business card, so that other users can see the contact's cloud business card when contacting the contact.
  • information such as the name and address of A is stored in the user A cloud business card, and the name, address, and the like of A are stored in the user B contact.
  • the cloud business card of A matches the contact of A stored in the user B contact, the cloud business card of A is recommended to B.
  • the information such as the name, address, and the like of the A stored in the cloud business card of the user A may not match the information such as the name, address, and the like of the contact A stored in the user B.
  • user A is the father of user B, and user A uploads his own cloud business card, including: “name” is “Wang 2”, but in the contact of user B, the “name” of contact A is “Dad”.
  • the user's cloud business card cannot match the information of the contact A in the user B.
  • User A's "address” is the residential address of A, and the "address" of A in user B's contact is the company address of A. Therefore, user A's cloud business card cannot match the information of contact A in user B.
  • Embodiments of the present disclosure provide a cloud business card recommendation method.
  • the accuracy of the recommended cloud business card is improved.
  • the cloud business card of the first contact that matches the contact avatar is obtained in the server, and the cloud business card of the first contact is pushed to the second contact.
  • Whether the cloud business card in the server matches the contact information stored in the terminal of the second contact is determined according to the avatar, and the accuracy of matching the cloud business card with the contact information when the cloud business card is recommended to the second contact is improved.
  • FIG. 1 is a flowchart of a cloud business card recommendation method according to an exemplary embodiment. As shown in FIG. 1 , a cloud business card recommendation method may be used in a server, including the following steps.
  • step S11 the cloud business card of the first contact and the contact information stored in the terminal of the second contact are obtained, and the cloud business card includes a cloud business card avatar, and the contact information includes a contact avatar.
  • step S12 the cloud business card avatar and the contact avatar are compared.
  • step S13 when the cloud business card avatar matches the contact avatar, the cloud business card of the first contact is recommended to the second contact.
  • Embodiments of the present disclosure provide a cloud business card recommendation method.
  • the cloud business card of the first contact that matches the contact avatar is obtained in the server, and the cloud business card of the first contact is pushed to the second contact.
  • Whether the cloud business card in the server matches the contact information stored in the terminal of the second contact is determined according to the avatar, and the accuracy of matching the cloud business card with the contact information when the cloud business card is recommended to the second contact is improved.
  • step S12 is further implemented as: Step A1 - Step A2.
  • step A1 the first similarity of the cloud business card avatar and the contact avatar is calculated.
  • step A2 when the first similarity reaches the preset similarity, it is determined that the cloud business card avatar matches the contact avatar.
  • the first similarity it is determined whether the cloud business card avatar matches the contact avatar.
  • the cloud business card avatar of the same person does not necessarily match the contact avatar. Therefore, when the first similarity reaches the preset similarity, it can be determined that the cloud business card and the contact information are the same person, further improving the recommendation of the cloud business card to the first The accuracy of matching the cloud business card with the contact information when the second contact is used.
  • step S12 is further implemented as: Step B1 - Step B3.
  • step B1 the face image is extracted from the cloud business card avatar and the contact avatar, respectively.
  • step B2 the cloud business card avatar and the face image of the contact avatar are compared.
  • step B3 when the cloud business card avatar matches the face image of the contact avatar, it is determined that the cloud business card avatar matches the contact avatar.
  • the cloud business card avatar and the contact avatar match by determining the face image extracted from the cloud business card avatar and the contact avatar.
  • the cloud business card avatar matches the face image of the contact avatar
  • the cloud business card can be determined
  • the contact information is the same person, which improves the speed of judging whether the face image of the cloud business card avatar and the contact avatar match, and further improves the accuracy of matching the cloud business card with the contact information when recommending the cloud business card to the second contact. .
  • step S12 is further implemented as: Step C1 - Step C2.
  • step C1 when the first similarity reaches the preset similarity, the number of occurrences of the cloud business card avatar or the contact avatar in all cloud business card avatars and contact avatars stored by the server is determined.
  • step C2 when the number of occurrences is less than or equal to the preset number of times, it is determined that the cloud business card avatar matches the contact avatar.
  • step C2 the method may also be implemented as: step D1 - step D3.
  • step D1 when the number of occurrences is greater than the preset number of times, the second similarity of the cloud business card avatar and the contact avatar is calculated according to the number of occurrences and the first similarity, and the second occurrence degree is higher than the first similarity. The smaller the degree.
  • step D2 it is determined whether the second similarity reaches a preset similarity.
  • step D3 when the second similarity does not reach the preset similarity, it is determined that the cloud business card avatar does not match the contact avatar.
  • the second similarity between the cloud business card avatar and the contact avatar is calculated by the number of occurrences of the cloud business card avatar or the contact avatar, and when the second similarity is less than the preset similarity, the cloud business card avatar and the contact avatar are not determined. match. Since many unmatched contacts will select some matching web images as contact avatars or cloud business card avatars. When the number of times the cloud business card avatar or the contact avatar appears is smaller, the similarity between the cloud business card avatar and the contact avatar is smaller, and the avatar may be determined as multiple contacts or cloud business card avatars instead of the same person's cloud business card or contact. Human avatar. Therefore, when the second similarity is less than the preset similarity, it is determined that the cloud business card avatar does not match the contact avatar, which further improves the accuracy of determining whether the cloud business card avatar matches the contact avatar.
  • the method may also be implemented as: Steps E1-E2.
  • step E1 the cloud business card avatar and the contact avatar are normalized to obtain a standard avatar of the cloud business card avatar and the contact avatar.
  • step E2 the feature points are extracted according to a preset rule for the standard avatar.
  • step S12 can also be implemented as: step E3 - step E4.
  • step E3 the feature points of the cloud business card avatar and the standard avatar of the contact avatar are compared.
  • step E4 it is determined whether the cloud business card avatar matches the contact avatar according to the comparison result of the feature points.
  • the speed of comparing the cloud business card avatar with the contact avatar can be improved, and the cloud business card can be improved.
  • the accuracy of whether the avatar matches the contact avatar further improves the accuracy of matching the cloud business card with the contact information when recommending the cloud business card to the second contact.
  • the method may also be implemented as: Steps F1-F2.
  • step F1 the face image of the cloud business card avatar and the contact avatar are normalized to obtain a standard face image of the cloud business card avatar and the contact avatar.
  • step F2 the feature points are extracted according to a preset rule for the standard face image.
  • step B2 can also be implemented as: steps F3-F4.
  • step F3 the feature points of the standard face image of the cloud business card avatar and the contact avatar are compared.
  • step F4 it is determined whether the cloud image avatar and the face image of the contact avatar match according to the comparison result of the feature points.
  • the speed of comparing the cloud business card avatar with the contact avatar can be improved, and the judgment can be improved.
  • the accuracy of matching the cloud business card avatar with the contact avatar further improves the accuracy of matching the cloud business card with the contact information when recommending the cloud business card to the second contact.
  • the embodiment of the present disclosure provides a cloud business card recommendation method, and the specific implementation steps are as follows:
  • step S21 the cloud business card of the first contact and the contact information stored in the terminal of the second contact are obtained, and the cloud business card includes a cloud business card avatar, and the contact information includes a contact avatar.
  • the server obtains the cloud business card of the user A from the server locally, and the contact information C stored by the user B obtained from the terminal of the user B.
  • step S22 a first similarity of the cloud business card avatar and the contact avatar is calculated.
  • the first similarity of the avatar of the cloud business card of the user A to the avatar of the contact C is 0.8.
  • step S23 when the first similarity reaches the preset similarity, the number of occurrences of the cloud business card avatar or the contact avatar in all cloud business card avatars and contact avatars stored by the server is determined.
  • steps S24-S25 are performed; when the number of occurrences is greater than the preset number of times, steps S26-S28 are performed.
  • the preset similarity is 0.6, so the first similarity between the avatar of the user's cloud business card and the avatar of the contact C reaches a preset similarity. At this time, it is determined that the number of times the avatar of the user's cloud business card appears in the cloud business card avatar and the contact avatar stored in the server is 4 times. The number of times the contact avatar appears in the cloud business card avatar and contact avatar stored in the server is 6 times.
  • step S24 when the number of occurrences is less than or equal to the preset number of times, it is determined that the cloud business card avatar matches the contact avatar.
  • the preset number is 20 times. It can be seen that the number of occurrences of the avatar of the user's cloud business card in the cloud business card avatar and the contact avatar stored in the server and the number of times the avatar of the contact C appears in the cloud business card avatar and the contact avatar stored in the server are smaller than the preset. The number of times, at this time, it can be determined that the cloud business card avatar of the user A matches the contact C avatar.
  • step S25 when the cloud business card avatar matches the contact avatar, the cloud business card of the first contact is recommended to the second contact.
  • the avatar of the user A's cloud business card matches the contact C avatar, it can be determined that the user A and the contact C are the same person. At this time, the user's cloud business card can be recommended to the user B, and the user B can store the information of the user A in the contact C.
  • step S26 when the number of occurrences is greater than the preset number of times, the second similarity of the cloud business card avatar and the contact avatar is calculated according to the number of occurrences and the first similarity, the higher the number of occurrences, the second similarity is similar to the first similarity The smaller the degree.
  • the number of times is 30 times.
  • the number of contacts C's avatar appears 60 times in the cloud business card avatar and contact avatar stored in the server.
  • the number of occurrences can be divided into different levels, each level corresponding to different weights, the weight is less than 1, and the number of occurrences is inversely proportional to the weight, that is, the weight decreases as the number of occurrences increases.
  • the first similarity is multiplied by the weight to obtain a second similarity.
  • Second similarity first similarity x weight.
  • step S27 it is determined whether the second similarity reaches a preset similarity.
  • step S28 when the second similarity does not reach the preset similarity, it is determined that the cloud business card avatar does not match the contact avatar.
  • the second similarity 0.4 does not reach the value of the preset similarity of 0.6, it can be determined that the avatar of the user A cloud business card and the contact C avatar are two different users sharing a similar avatar. Therefore, it is determined that the user A cloud business card avatar does not match the contact C avatar.
  • Embodiments of the present disclosure provide a cloud business card recommendation method.
  • the cloud business card of the first contact that matches the contact avatar is obtained in the server, and the cloud business card of the first contact is pushed to the second contact.
  • By determining the first similarity it is determined whether the cloud business card avatar matches the contact avatar.
  • the cloud business card avatar of the same person does not necessarily match the contact avatar. Therefore, when the first similarity reaches the preset similarity, it can be determined that the cloud business card and the contact information are the same person, and when the first similarity reaches the preset In the similarity degree, the probability of different contacts using the same avatar is excluded by judging the number of occurrences of the avatar or contact avatar of the cloud business card and the second similarity. Therefore, the accuracy of determining whether the cloud business card avatar matches the contact avatar is improved by the above method, and the cloud business card of the first contact is accurately recommended to the second contact.
  • an embodiment of the present disclosure provides a cloud business card recommendation method, and the specific implementation steps are as follows:
  • step S31 the cloud business card of the first contact and the contact information stored in the terminal of the second contact are obtained, and the cloud business card includes a cloud business card avatar, and the contact information includes a contact avatar.
  • the server obtains the cloud business card of the user A from the server locally, and the contact information C stored by the user B obtained from the terminal of the user B.
  • step S32 the face image is extracted from the cloud card avatar and the contact avatar, respectively.
  • the face images A1 and C1 are extracted from the avatar of the user card of the user A and the avatar of the contact C, respectively.
  • step S33 the cloud business card avatar and the face image of the contact avatar are compared.
  • the face image A1 and the face image C1 are compared.
  • step S34 when the cloud business card avatar and the face image of the contact avatar match, it is determined that the cloud business card avatar matches the contact avatar.
  • step S35 when the cloud business card avatar matches the contact avatar, the cloud business card of the first contact is recommended to the second contact.
  • the avatar of the user A's cloud business card matches the contact C avatar, it can be determined that the user A and the contact C are the same person. At this time, the user's cloud business card can be recommended to the user B, and the user B can store the information of the user A in the contact C.
  • Embodiments of the present disclosure provide a cloud business card recommendation method. Obtaining a cloud business card of the first contact that matches the contact avatar in the server, determining a face image extracted from the cloud business card avatar and the contact avatar, determining whether the cloud business card avatar matches the contact avatar, and when the cloud business card avatar and the contact When the face image of the human avatar matches, the cloud business card and the contact information are determined to be the same person, and the cloud business card of the first contact is pushed to the second contact. The speed of judging whether the face image of the cloud business card avatar and the contact avatar matches is improved, and the accuracy of matching the cloud business card with the contact information when the cloud business card is recommended to the second contact is further improved.
  • the embodiment of the present disclosure provides a cloud business card recommendation method, and the specific implementation steps are as follows:
  • step S41 the cloud business card of the first contact and the contact information stored in the terminal of the second contact are obtained, and the cloud business card includes a cloud business card avatar, and the contact information includes a contact avatar.
  • the server obtains the cloud business card of the user A from the server locally, and the contact information C stored by the user B obtained from the terminal of the user B.
  • step S42 the cloud business card avatar and the contact avatar are normalized to obtain a standard avatar of the cloud business card avatar and the contact avatar.
  • the avatar of the cloud business card of the user A and the avatar of the contact C can be normalized to obtain the standard avatar D1 of the cloud business card of the user A, and the standard avatar D2 of the avatar of the contact C. .
  • step S43 the feature points are extracted according to a preset rule for the standard avatar.
  • the same number of feature points are extracted from the standard avatars D1 and D2, for example, the number of extracted feature points is 100, but the disclosure is not limited thereto, and a different number of feature points may also be extracted.
  • the feature point set 1 is extracted from the standard avatar D1, and the extracted feature point set 1 is stored; the feature point set 2 is extracted from the standard avatar D2, and the extracted feature point set 2 is stored.
  • step S44 the feature points of the cloud business card avatar and the standard avatar of the contact avatar are compared.
  • the feature point set 1 and the feature point set 2 are compared, and it is known that the feature point set 1 and the feature point set 2 have 78 identical feature points.
  • step S45 it is determined whether the cloud business card avatar matches the contact avatar according to the comparison result of the feature points.
  • the feature point set 1 and the feature point set 2 have 78 identical feature points, and it can be determined that the avatar of the user A's cloud business card matches the avatar of the contact C.
  • step S46 when the cloud business card avatar matches the contact avatar, the cloud business card of the first contact is recommended to the second contact.
  • the avatar of the user A's cloud business card matches the contact C avatar, it can be determined that the user A and the contact C are the same person. At this time, the user's cloud business card can be recommended to the user B, and the user B can store the information of the user A in the contact C.
  • Embodiments of the present disclosure provide a cloud business card recommendation method.
  • the cloud business card of the first contact that matches the contact avatar is obtained in the server, and the feature points of the standard avatar of the cloud business card avatar and the contact avatar are compared, and whether the cloud business card avatar matches the contact avatar is determined, and the comparison cloud business card avatar can be improved.
  • the speed of the contact avatar can also improve the accuracy of determining whether the cloud business card avatar matches the contact avatar, and further improves the accuracy of matching the cloud business card with the contact information when recommending the cloud business card to the second contact.
  • the embodiment of the present disclosure provides a cloud business card recommendation method, and the specific implementation steps are as follows:
  • step S51 the cloud business card of the first contact and the contact information stored in the terminal of the second contact are obtained, and the cloud business card includes a business card avatar, and the contact information includes a contact avatar.
  • the server obtains the cloud business card of the user A from the server locally, and the contact information C stored by the user B obtained from the terminal of the user B.
  • step S52 the face image is extracted from the cloud business card avatar and the contact avatar, respectively.
  • the face image E1 is extracted from the avatar of the user card of the user A, and the face image E2 is extracted from the avatar of the contact C.
  • step S53 the face image of the cloud business card avatar and the contact avatar are normalized to obtain a standard face image of the cloud business card avatar and the contact avatar.
  • the face image E1 of the cloud business card of the user A and the face image E2 of the contact C are normalized to obtain the standard face image F1 of the cloud business card of the user A, and the standard face image F2 of the avatar of the contact C .
  • step S54 the feature points are extracted according to a preset rule for the standard face image.
  • the same number of feature points are extracted from the standard face images F1 and F2, and the number of extracted feature points is 100.
  • the present disclosure is not limited thereto, and a different number of feature points may also be extracted.
  • the feature point set 1 is extracted from the standard face image F1, and the extracted feature point set 1 is stored; the feature point set 2 is extracted from the standard face image F2, and the extracted feature point set 2 is stored.
  • step S55 the feature points of the standard business face image of the cloud business card avatar and the contact avatar are compared.
  • the feature point set 1 and the feature point set 2 are compared, and it is known that the feature point set 1 and the feature point set 2 have 78 matching feature points.
  • step S56 it is determined whether the cloud image avatar and the face image of the contact avatar match according to the comparison result of the feature points.
  • the feature point set 1 and the feature point set 2 have 78 matching feature points, and it can be determined that the face image E1 of the avatar of the user A's cloud business card matches the face image E2 of the avatar of the contact C.
  • step S57 when the cloud business card avatar and the face image of the contact avatar match, it is determined that the cloud business card avatar matches the contact avatar.
  • step S58 when the cloud business card avatar matches the contact avatar, the cloud business card of the first contact is recommended to the second contact.
  • the avatar of the user A's cloud business card matches the contact C avatar, it can be determined that the user A and the contact C are the same person. At this time, the user's cloud business card can be recommended to the user B, and the user B can store the information of the user A in the contact C.
  • Embodiments of the present disclosure provide a cloud business card recommendation method.
  • Get the first match in the server that matches the contact avatar The cloud business card of the person is determined by judging the face image extracted from the cloud business card avatar and the contact avatar, and comparing the feature points of the standard business face image of the cloud business card avatar and the contact avatar to determine whether the cloud business card avatar matches the contact avatar.
  • the cloud business card avatar matches the face image of the contact avatar it can be determined that the cloud business card and the contact information are the same person, the speed of comparing the cloud business card avatar and the contact avatar is improved, and the cloud business card avatar and the contact person can be improved.
  • the accuracy of whether the avatar matches further improves the accuracy of matching the cloud business card with the contact information when recommending the cloud business card to the second contact.
  • the cloud business card avatar matches the contact avatar, for example, including the cloud business card avatar and the contact avatar having the same face; or the cloud business card avatar and the contact avatar are the same image; or the cloud business card avatar and The similarity between the contact avatars exceeds a certain threshold.
  • FIG. 6 is a block diagram of a cloud business card recommending apparatus according to an exemplary embodiment. As shown in Figure 6, the device includes:
  • the obtaining module 61 is configured to acquire a cloud business card of the first contact, and contact information stored in the terminal of the second contact, where the cloud business card includes a cloud business card avatar, and the contact information includes a contact avatar:
  • the comparison module 62 is configured to compare the cloud business card avatar with the contact avatar:
  • the recommendation module 63 is configured to recommend the cloud card of the first contact to the second contact when the cloud business card avatar matches the contact avatar.
  • the comparison module 62 includes:
  • the first calculation sub-module 71 is configured to calculate a first similarity of the cloud business card avatar and the contact avatar.
  • the first determining sub-module 72 is configured to determine that the cloud business card avatar matches the contact avatar when the first similarity reaches a preset similarity.
  • the comparison module 62 includes:
  • the extraction sub-module 81 is configured to extract a face image from the cloud business card avatar and the contact avatar, respectively.
  • the comparison sub-module 82 is configured to compare the cloud business card avatar with the face image of the contact avatar.
  • the second determining sub-module 83 is configured to determine that the cloud business card avatar matches the contact avatar when the cloud business card avatar and the contact avatar face image match.
  • the comparison module 62 further includes:
  • the third determining sub-module 91 is configured to determine the number of occurrences of the cloud business card avatar or the contact avatar in all cloud business card avatars and contact avatars stored by the server when the first similarity reaches the preset similarity.
  • the fourth determining sub-module 92 is configured to determine that the cloud business card avatar matches the contact avatar when the number of occurrences is less than or equal to the preset number of times.
  • the comparison module further includes:
  • the second calculation sub-module 101 is configured to calculate a second similarity between the cloud business card avatar and the contact avatar according to the number of occurrences and the first similarity when the number of occurrences is greater than the preset number of times, the higher the number of occurrences, the second similarity is relative
  • the smaller the first similarity is:
  • the judgment sub-module 102 is configured to determine whether the second similarity reaches a preset similarity.
  • the fifth determining sub-module 103 is configured to determine that the cloud business card avatar does not match the contact avatar when the second similarity does not reach the preset similarity.
  • the device further includes:
  • the first processing module 111 is configured to normalize the cloud business card avatar and the contact avatar to obtain a standard avatar of the cloud business card avatar and the contact avatar:
  • the first extraction module 112 is configured to extract feature points according to a preset rule for the standard avatar.
  • the comparison module 62 includes:
  • the comparison sub-module 113 is configured to compare feature points of the cloud business card avatar and the standard avatar of the contact avatar:
  • the sixth determining sub-module 114 is configured to determine whether the cloud business card avatar matches the contact avatar according to the comparison result of the feature points.
  • the device further includes:
  • the second processing module 121 is configured to normalize the face image of the cloud business card avatar and the contact avatar to obtain a standard face image of the cloud business card avatar and the contact avatar.
  • the second extraction module 122 is configured to extract feature points from the standard face image according to a preset rule.
  • the comparison sub-module 82 is further configured to compare the feature points of the cloud business card avatar and the standard face image of the contact avatar; and determine whether the cloud business card avatar and the contact avatar face image match according to the comparison result of the feature points.
  • FIG. 13 is a block diagram of an apparatus 1300 for cloud business card recommendation, according to an exemplary embodiment.
  • device 1300 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a gaming console, a tablet device, a medical device, a fitness device, a personal digital assistant, and the like.
  • device 1300 can include one or more of the following components: processing component 1302, memory 1304, power component 1306, multimedia component 1308, audio component 1310, input/output (I/O) interface 1312, sensor component 1314, and a communication component 1316.
  • processing component 1302 memory 1304, power component 1306, multimedia component 1308, audio component 1310, input/output (I/O) interface 1312, sensor component 1314, and a communication component 1316.
  • Processing component 1302 typically controls the overall operation of device 1300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • Processing component 1302 can include one or more processors 1320 to execute the fingers Order to complete all or part of the steps of the above method.
  • processing component 1302 can include one or more modules to facilitate interaction between component 1302 and other components.
  • processing component 1302 can include a multimedia module to facilitate interaction between multimedia component 1308 and processing component 1302.
  • Memory 1304 is configured to store various types of data to support operation at device 1300. Examples of such data include instructions for any application or method operating on device 1300, contact data, phone book data, messages, pictures, videos, and the like.
  • Memory 1304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Disk Disk or Optical Disk.
  • Power component 1306 provides power to various components of device 1300.
  • Power component 1306 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for device 1300.
  • the multimedia component 1308 includes a screen between the device 1300 and the user that provides an output interface.
  • the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can sense not only the boundaries of the touch or sliding action, but also the duration and pressure associated with the touch or slide operation.
  • the multimedia component 1308 includes a front camera and/or a rear camera. When the device 1300 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 1310 is configured to output and/or input an audio signal.
  • the audio component 1310 includes a microphone (MIC) that is configured to receive an external audio signal when the device 1300 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode.
  • the received audio signal may be further stored in memory 1304 or transmitted via communication component 1316.
  • the audio component 1310 also includes a speaker for outputting an audio signal.
  • the I/O interface 1312 provides an interface between the processing component 1302 and the peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to, a home button, a volume button, a start button, and a lock button.
  • Sensor assembly 1314 includes one or more sensors for providing device 1300 with a status assessment of various aspects.
  • the sensor assembly 1314 can detect an open/closed state of the device 1300, the relative positioning of the components, such as a display and a keypad of the device 1300, and the sensor component 1314 can also detect a change in position of a component of the device 1300 or device 1300, the user The presence or absence of contact with device 1300, device 1300 orientation or acceleration/deceleration and loading Set the temperature change of 1300.
  • Sensor assembly 1314 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • Sensor assembly 1314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 1314 can also include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 1316 is configured to facilitate wired or wireless communication between device 1300 and other devices.
  • the device 1300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof.
  • communication component 1316 receives broadcast signals or broadcast associated information from an external broadcast management system via a broadcast channel.
  • communication component 1316 also includes a near field communication (NFC) module to facilitate short range communication.
  • NFC near field communication
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • apparatus 1300 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A gate array (FPGA), controller, microcontroller, microprocessor, or other electronic component implementation for performing the above methods.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A gate array
  • controller microcontroller, microprocessor, or other electronic component implementation for performing the above methods.
  • non-transitory computer readable storage medium comprising instructions, such as a memory 1304 comprising instructions executable by processor 1320 of apparatus 1300 to perform the above method.
  • the non-transitory computer readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.
  • a non-transitory computer readable storage medium when executed by a processor of a mobile terminal, enables the mobile terminal to perform a cloud business card recommendation method, the method comprising:
  • the first contact's cloud business card is recommended to the second contact.
  • the cloud business card avatar is compared with the contact avatar, including:
  • the cloud business card avatar matches the contact avatar.
  • Compare the cloud business card avatar with the contact avatar including:
  • comparing the cloud business card avatar with the contact avatar further includes:
  • comparing the cloud business card avatar with the contact avatar includes:
  • the method also includes:
  • Compare the cloud business card avatar with the contact avatar including:
  • the method also includes:
  • Compare the cloud business card avatar with the face image of the contact avatar including:
  • FIG. 14 is a block diagram of an apparatus 1400 for cloud business card recommendation, according to an exemplary embodiment.
  • device 1400 can be provided as a server.
  • apparatus 1400 includes a processing component 1422 that further includes one or more processors, and memory resources represented by memory 1432 for storing instructions executable by processing component 1422, such as an application.
  • the application stored in the memory 1432 may include one or more modules each corresponding to a set of instructions.
  • processing component 1422 is configured to execute instructions to perform the methods described below:
  • the cloud business card includes a business card avatar, and the contact information includes a contact avatar;
  • the first contact's cloud business card is recommended to the second contact.
  • Compare the cloud business card avatar with the contact avatar including:
  • the cloud business card avatar matches the contact avatar.
  • Compare the cloud business card avatar with the contact avatar including:
  • comparing the cloud business card avatar with the contact avatar further includes:
  • comparing the cloud business card avatar with the contact avatar includes:
  • the method also includes:
  • Compare the cloud business card avatar with the contact avatar including:
  • the method also includes:
  • Compare the cloud business card avatar with the face image of the contact avatar including:
  • Apparatus 1400 can also include a power supply component 1426 configured to perform power management of apparatus 1400, a wired or wireless network interface 1450 configured to connect apparatus 1400 to the network, and an input/output (I/O) interface 1458.
  • the device 1400 can operate based on an operating system stored in the memory 1432, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

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Abstract

本公开是关于一种云名片推荐方法及装置，所述方法包括：获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括云名片头像，所述联系人信息中包括联系人头像；将所述云名片头像和所述联系人头像进行比对；当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。用以将联系人的云名片推荐给其他用户时，提高推荐云名片的准确性。

Description

云名片推荐方法及装置
相关申请的交叉引用
本申请基于申请号为201510276603.9、申请日为2015年05月26日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本公开涉及计算机技术领域，尤其涉及一种云名片推荐方法及装置。
背景技术
相关技术中，随着计算机技术的快速发展，智能终端已经成为人们生活中的日常用品，而在智能终端中可以安装一些社交应用，用户在社交应用中记录着不匹配联系人的联系人信息及联系人头像，而且用户可以将这些联系人信息及联系人头像上传到服务器生成云名片，并公开云名片，使其他用户可以看到该联系人的云名片。
发明内容
本公开实施例提供一种云名片推荐方法及装置。用以将联系人的云名片推荐给其他用户时，提高推荐云名片的准确性。
根据本公开实施例的第一方面，提供一种云名片推荐方法，所述方法包括：
获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括名片头像，所述联系人信息中包括联系人头像；
将所述云名片头像和所述联系人头像进行比对；
当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。
本公开的实施例提供的技术方案可以包括以下有益效果：在服务器中获取与联系人头像匹配的第一联系人的云名片，将第一联系人的云名片推送给第二联系人。根据头像判断服务器中的云名片是否与第二联系人的终端中存储的联系人信息匹配，提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
将所述云名片头像和所述联系人头像进行比对，包括：
计算所述云名片头像和所述联系人头像的第一相似度；
当所述第一相似度达到预设相似度时，确定所述云名片头像与所述联系人头像匹配。
本公开的实施例提供的技术方案可以包括以下有益效果：通过判断第一相似度，确定云名片头像与联系人头像是否匹配。同一人的云名片头像与联系人头像并不一定匹配，因此，当第一相似度达到预设相似度时，可以确定云名片与联系人信息是同一人，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
将所述云名片头像和所述联系人头像进行比对，包括：
分别从所述云名片头像和所述联系人头像中提取人脸图像；
将所述云名片头像和所述联系人头像的人脸图像进行比对；
当所述云名片头像和所述联系人头像的人脸图像匹配时，确定所述云名片头像与所述联系人头像匹配。
本公开的实施例提供的技术方案可以包括以下有益效果：通过判断云名片头像和联系人头像中提取的人脸图像，确定云名片头像与联系人头像是否匹配。当云名片头像和联系人头像的人脸图像匹配时，可以确定云名片与联系人信息是同一人，提高了判断云名片头像和联系人头像的人脸图像是否匹配的速度，而且进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
当所述第一相似度达到预设相似度时，将所述云名片头像和所述联系人头像进行比对还包括：
确定所述云名片头像或所述联系人头像在所述服务器存储的所有云名片头像和联系人头像中的出现次数；
当所述出现次数小于或等于预设次数时，确定所述云名片头像与所述联系人头像匹配。
本公开的实施例提供的技术方案可以包括以下有益效果：不同的联系人可能会选择一些相同的网络图片作为联系人头像或云名片头像。当云名片头像或联系人头像出现的次数大于预设次数时，可以确定该头像为多个联系人或云名片头像，而不是同一个人的云名片或联系人头像。因此，当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配，进一步提高了判断云名片头像与联系人头像是否匹配的准确度。
当所述出现次数大于预设次数时，将所述云名片头像和所述联系人头像进行比对还包括：
根据所述出现次数和所述第一相似度计算所述云名片头像和所述联系人头像的第二相似度，所述出现次数越高，所述第二相似度相对于所述第一相似度越小；
判断所述第二相似度是否达到所述预设相似度；
当所述第二相似度未达到所述预设相似度时，确定所述云名片头像与所述联系人头像不匹配。
本公开的实施例提供的技术方案可以包括以下有益效果：通过云名片头像或联系人头像出现次数计算云名片头像和联系人头像的第二相似度，并在第二相似度小于预设相似度时，确定云名片头像与联系人头像不匹配。不同的联系人可能会选择一些相同的网络图片作为联系人头像或云名片头像。当云名片头像或联系人头像出现的次数越多则云名片头像和联系人头像的相似度越小，可以确定该头像为多个联系人或云名片头像，而不是同一个人的云名片或联系人头像。因此，在第二相似度小于预设相似度时，确定云名片头像与联系人头像不匹配，进一步提高了判断云名片头像与联系人头像是否匹配的准确度。
所述方法还包括：
对所述云名片头像和所述联系人头像进行归一化处理，得到所述云名片头像和所述联系人头像的标准头像；
对所述标准头像按照预设规则提取特征点；
所述将所述云名片头像和所述联系人头像进行比对，包括：
比较所述云名片头像和所述联系人头像的标准头像的特征点；
根据特征点的比较结果确定所述云名片头像与所述联系人头像是否匹配。
本公开的实施例提供的技术方案可以包括以下有益效果：通过比较云名片头像和联系人头像的标准头像的特征点，确定云名片头像与联系人头像是否匹配，可以提高对比云名片头像与联系人头像的速度，还可以提高判断云名片头像与联系人头像是否匹配的准确度，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
所述方法还包括：
对所述云名片头像和所述联系人头像的人脸图像进行归一化处理，得到所述云名片头像和所述联系人头像的标准人脸图像；
对所述标准人脸图像按照预设规则提取特征点；
所述将所述云名片头像和所述联系人头像的人脸图像进行比对，包括：
比较所述云名片头像和所述联系人头像的标准人脸图像的特征点；
根据特征点的比较结果确定所述云名片头像和所述联系人头像的人脸图像是否匹配。
本公开的实施例提供的技术方案可以包括以下有益效果：通过比较云名片头像和联系人头像的标准人脸图像的特征点，确定云名片头像与联系人头像是否匹配，可以提高对比云名片头像与联系人头像的速度，还可以提高判断云名片头像与联系人头像是否匹配的准确度， 进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
根据本公开实施例的第二方面，提供一种云名片推荐装置，所述装置包括：
获取模块，用于获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括名片头像，所述联系人信息中包括联系人头像；
对比模块，用于将所述云名片头像和所述联系人头像进行比对；
推荐模块，用于当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。
对比模块，包括：
第一计算子模块，用于计算所述云名片头像和所述联系人头像的第一相似度；
第一确定子模块，用于当所述第一相似度达到预设相似度时，确定所述云名片头像与所述联系人头像匹配。
对比模块，包括：
提取子模块，用于分别从所述云名片头像和所述联系人头像中提取人脸图像；
对比子模块，用于将所述云名片头像和所述联系人头像的人脸图像进行比对；
第二确定子模块，用于当所述云名片头像和所述联系人头像的人脸图像匹配时，确定所述云名片头像与所述联系人头像匹配。
对比模块还包括：
第三确定子模块，用于当所述第一相似度达到预设相似度时，确定所述云名片头像或所述联系人头像在所述服务器存储的所有云名片头像和联系人头像中的出现次数；
第四确定子模块，用于当所述出现次数小于或等于预设次数时，确定所述云名片头像与所述联系人头像匹配。
对比模块还包括：
第二计算子模块，用于当所述出现次数大于预设次数时，根据所述出现次数和所述第一相似度计算所述云名片头像和所述联系人头像的第二相似度，所述出现次数越高，所述第二相似度相对于所述第一相似度越小；
判断子模块，用于判断所述第二相似度是否达到所述预设相似度；
第五确定子模块，用于当所述第二相似度未达到所述预设相似度时，确定所述云名片头像与所述联系人头像不匹配。
所述装置还包括：
第一处理模块，用于对所述云名片头像和所述联系人头像进行归一化处理，得到所述云 名片头像和所述联系人头像的标准头像；
第一提取模块，用于对所述标准头像按照预设规则提取特征点；
所述对比模块，包括：
比较子模块，用于比较所述云名片头像和所述联系人头像的标准头像的特征点；
第六确定子模块，用于根据特征点的比较结果确定所述云名片头像与所述联系人头像是否匹配。
所述装置还包括：
第二处理模块，用于对所述云名片头像和所述联系人头像的人脸图像进行归一化处理，得到所述云名片头像和所述联系人头像的标准人脸图像；
第二提取模块，用于对所述标准人脸图像按照预设规则提取特征点；
所述对比子模块，还用于比较所述云名片头像和所述联系人头像的标准人脸图像的特征点；根据特征点的比较结果确定所述云名片头像和所述联系人头像的人脸图像是否匹配。
根据本公开实施例的第三方面，提供一种云名片推荐装置，包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为：
获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括名片头像，所述联系人信息中包括联系人头像；
将所述云名片头像和所述联系人头像进行比对；
当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
图1是根据一示例性实施例示出的一种云名片推荐方法的流程图。
图2是根据一示例性实施例示出的一种云名片推荐方法的详细流程图。
图3是根据一示例性实施例示出的一种云名片推荐方法的详细流程图。
图4是根据一示例性实施例示出的一种云名片推荐方法的详细流程图。
图5是根据一示例性实施例示出的一种云名片推荐方法的详细流程图。
图6是根据一示例性实施例示出的一种云名片推荐装置的框图。
图7是根据一示例性实施例示出的一种云名片推荐装置中对比模块的框图。
图8是根据一示例性实施例示出的一种云名片推荐装置中对比模块的框图。
图9是根据一示例性实施例示出的一种云名片推荐装置中对比模块的框图。
图10是根据一示例性实施例示出的一种云名片推荐装置中对比模块的框图。
图11是根据一示例性实施例示出的一种云名片推荐装置的框图。
图12是根据一示例性实施例示出的一种云名片推荐装置的框图。
图13是根据一示例性实施例示出的一种云名片推荐装置的框图。
图14是根据一示例性实施例示出的一种云名片推荐装置的框图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。
相关技术中，随着计算机技术的快速发展，智能终端已经成为人们生活中的日常用品，而在智能终端中可以安装一些社交应用，用户在社交应用中记录着不同联系人的联系人信息及联系人头像，而且用户可以将这些联系人信息及联系人头像上传到服务器生成云名片，并公开云名片，使其他用户与该联系人联系时可以看到该联系人的云名片。
例如，在用户A云名片中存储有A的姓名、地址等信息，而用户B联系人中存储有A的姓名、地址等信息。判断A的云名片与用户B联系人中存储的A的联系人匹配时，会将A的云名片推荐给B。
但是，在相关技术中存在用户A的云名片中存储的A的姓名、地址等信息可能与用户B中存储的联系人A的姓名、地址等信息不匹配的情况。
例如，用户A是用户B的父亲，用户A上传自己的云名片，包括：“姓名”为“王二”，但在用户B的联系人中，联系人A的“姓名”为“爸爸”。这样，用户A的云名片便无法和用户B中的联系人A的信息匹配。用户A的“地址”为A的住宅地址，而用户B联系人中A的“地址”为A的公司地址，因此，用户A的云名片也无法和用户B中的联系人A的信息匹配。
本公开实施例提供了一种云名片推荐方法。用以将联系人的云名片推荐给其他用户时，提高推荐云名片的准确性。在服务器中获取与联系人头像匹配的第一联系人的云名片，将第一联系人的云名片推送给第二联系人。根据头像判断服务器中的云名片是否与第二联系人的终端中存储的联系人信息匹配，提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
图1是根据一示例性实施例示出的一种云名片推荐方法的流程图，如图1所示，云名片推荐方法可以用于服务器中，包括以下步骤。
在步骤S11中，获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括云名片头像，联系人信息中包括联系人头像。
在步骤S12中，将云名片头像和联系人头像进行比对。
在步骤S13中，当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
本公开实施例提供了一种云名片推荐方法。在服务器中获取与联系人头像匹配的第一联系人的云名片，将第一联系人的云名片推送给第二联系人。根据头像判断服务器中的云名片是否与第二联系人的终端中存储的联系人信息匹配，提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
可选的，在一个实施例中，步骤S12还可实施为：步骤A1-步骤A2。
在步骤A1中，计算云名片头像和联系人头像的第一相似度。
在步骤A2中，当第一相似度达到预设相似度时，确定云名片头像与联系人头像匹配。
采用上述方法，通过判断第一相似度，确定云名片头像与联系人头像是否匹配。同一人的云名片头像与联系人头像并不一定匹配，因此，当第一相似度达到预设相似度时，可以确定云名片与联系人信息是同一人，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
可选的，在一个实施例中，步骤S12还可实施为：步骤B1-步骤B3。
在步骤B1中，分别从云名片头像和联系人头像中提取人脸图像。
在步骤B2中，将云名片头像和联系人头像的人脸图像进行比对。
在步骤B3中，当云名片头像和联系人头像的人脸图像匹配时，确定云名片头像与联系人头像匹配。
采用上述方法，通过判断云名片头像和联系人头像中提取的人脸图像，确定云名片头像与联系人头像是否匹配。当云名片头像和联系人头像的人脸图像匹配时，可以确定云名片与 联系人信息是同一人，提高了判断云名片头像和联系人头像的人脸图像是否匹配的速度，而且进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
可选的，在一个实施例中，步骤S12还可实施为：步骤C1-步骤C2。
在步骤C1中，当第一相似度达到预设相似度时，确定云名片头像或联系人头像在服务器存储的所有云名片头像和联系人头像中的出现次数。
在步骤C2中，当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配。
采用上述方法，由于许多不匹配的联系人会选择一些匹配的网络图片作为联系人头像或云名片头像。当云名片头像或联系人头像出现的次数大于预设次数时，可以确定该头像为多个联系人或云名片头像，而不是同一个人的云名片或联系人头像。因此，当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配，进一步提高了判断云名片头像与联系人头像是否匹配的准确度。
可选的，在步骤C2之后，该方法还可实施为：步骤D1-步骤D3。
在步骤D1中，当出现次数大于预设次数时，根据出现次数和第一相似度计算云名片头像和联系人头像的第二相似度，出现次数越高，第二相似度相对于第一相似度越小。
在步骤D2中，判断第二相似度是否达到预设相似度。
在步骤D3中，当第二相似度未达到预设相似度时，确定云名片头像与联系人头像不匹配。
采用上述方法，通过云名片头像或联系人头像出现次数计算云名片头像和联系人头像的第二相似度，并在第二相似度小于预设相似度时，确定云名片头像与联系人头像不匹配。由于许多不匹配的联系人会选择一些匹配的网络图片作为联系人头像或云名片头像。当云名片头像或联系人头像出现的次数越多则云名片头像和联系人头像的相似度越小，可以确定该头像为多个联系人或云名片头像，而不是同一个人的云名片或联系人头像。因此，在第二相似度小于预设相似度时，确定云名片头像与联系人头像不匹配，进一步提高了判断云名片头像与联系人头像是否匹配的准确度。
可选的，在一个实施例中，该方法还可实施为：步骤E1-E2。
在步骤E1中，对云名片头像和联系人头像进行归一化处理，得到云名片头像和联系人头像的标准头像。
在步骤E2中，对标准头像按照预设规则提取特征点。
此时，步骤S12还可实施为：步骤E3-步骤E4。
在步骤E3中，比较云名片头像和联系人头像的标准头像的特征点。
在步骤E4中，根据特征点的比较结果确定云名片头像与联系人头像是否匹配。
采用上述方法，通过比较云名片头像和联系人头像的标准头像的特征点，确定云名片头像与联系人头像是否匹配，可以提高对比云名片头像与联系人头像的速度，还可以提高判断云名片头像与联系人头像是否匹配的准确度，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
可选的，在一个实施例中，该方法还可实施为：步骤F1-F2。
在步骤F1中，对云名片头像和联系人头像的人脸图像进行归一化处理，得到云名片头像和联系人头像的标准人脸图像。
在步骤F2中，对标准人脸图像按照预设规则提取特征点。
此时，步骤B2还可实施为：步骤F3-F4。
在步骤F3中，比较云名片头像和联系人头像的标准人脸图像的特征点。
在步骤F4中，根据特征点的比较结果确定云名片头像和联系人头像的人脸图像是否匹配。
采用上述方法，通过比较云名片头像和联系人头像的标准人脸图像的特征点，确定云名片头像与联系人头像是否匹配，可以提高对比云名片头像与联系人头像的速度，还可以提高判断云名片头像与联系人头像是否匹配的准确度，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
如图2所示，本公开实施例提供了一种云名片推荐方法，具体实施步骤如下：
在步骤S21中，获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括云名片头像，联系人信息中包括联系人头像。
服务器从服务器本地获得用户A的云名片，并从用户B的终端中获取的用户B存储的联系人信息C。
在步骤S22中，计算云名片头像和联系人头像的第一相似度。
计算用户A的云名片的头像与联系人C的头像的第一相似度为0.8。
在步骤S23中，当第一相似度达到预设相似度时，确定云名片头像或联系人头像在服务器存储的所有云名片头像和联系人头像中的出现次数。当出现次数小于或等于预设次数时，执行步骤S24-S25；当出现次数大于预设次数时执行步骤S26-S28。
预设相似度为0.6，因此用户A的云名片的头像与联系人C的头像的第一相似度达到预设相似度。此时确定用户A的云名片的头像在服务器存储的云名片头像和联系人头像中出现的次数为4次。联系人C的头像在服务器存储的云名片头像和联系人头像中出现的次数为 6次。
在步骤S24中，当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配。
预设次数为20次。可以看出用户A的云名片的头像在服务器存储的云名片头像和联系人头像中出现的次数及联系人C的头像在服务器存储的云名片头像和联系人头像中出现的次数均小于预设次数，此时可以确定用户A的云名片头像与联系人C头像匹配。
在步骤S25中，当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
当用户A的云名片的头像与联系人C头像匹配时，可以确定用户A与联系人C同一个人。此时可以将用户A的云名片推荐给用户B，供用户B在联系人C中存储用户A的信息。
在步骤S26中，当出现次数大于预设次数时，根据出现次数和第一相似度计算云名片头像和联系人头像的第二相似度，出现次数越高，第二相似度相对于第一相似度越小。
如果确定用户A的云名片的头像在服务器存储的云名片头像和联系人头像中出现的次数为30次。联系人C的头像在服务器存储的云名片头像和联系人头像中出现的次数为60次。根据用户A的云名片的头像出现次数、联系人C的头像出现次数及第一相似度计算用户A的云名片的头像与联系人C的头像的第二相似度，第二相似度与用户A的云名片的头像出现次数及联系人C的头像出现次数成反比。其中，可以将出现次数分为不同级别，每个级别对应不同权值，权值小于1，且出现次数与权值成反比，即随出现次数的增加权值减小。第一相似度乘以权值得到第二相似度。
第二相似度＝第一相似度×权值。
例如，在本公开实施例中用户A的云名片的头像出现的次数为30次，联系人C的头像出现的次数为60次，则等比较的头像出现次数为90次。假设头像出现次数为90次所对应的权值为0.5时，则计算得第二相似度为0.4。头像出现次数为200次所对应的权值为0.2，则计算得第二相似度为0.16。
在步骤S27中，判断第二相似度是否达到预设相似度。
可以看出第二相似度0.4未达到预设相似度0.6的值。
在步骤S28中，当第二相似度未达到预设相似度时，确定云名片头像与联系人头像不匹配。
由于第二相似度0.4未达到预设相似度0.6的值，可以确定用户A云名片的头像与联系人C头像是两个不同用户共用了相似的头像。因此，确定用户A云名片头像与联系人C头像不匹配。
本公开实施例提供了一种云名片推荐方法。在服务器中获取与联系人头像匹配的第一联系人的云名片，将第一联系人的云名片推送给第二联系人。通过判断第一相似度，确定云名片头像与联系人头像是否匹配。同一人的云名片头像与联系人头像并不一定匹配，因此，当第一相似度达到预设相似度时，可以确定云名片与联系人信息是同一人，而且当第一相似度达到预设相似度时，通过判断云名片的头像或联系人头像出现的次数及第二相似度，排除出不同联系人使用相同头像的机率。从而通过上述方法可以提高判断云名片头像与联系人头像是否匹配的准确度，并准确的将第一联系人的云名片推荐给第二联系人。
如图3所示，本公开实施例提供了一种云名片推荐方法，具体实施步骤如下：
在步骤S31中，获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括云名片头像，联系人信息中包括联系人头像。
服务器从服务器本地获得用户A的云名片，并从用户B的终端中获取的用户B存储的联系人信息C。
在步骤S32中，分别从云名片头像和联系人头像中提取人脸图像。
分别从用户A的云名片的头像及联系人C的头像中提取人脸图像A1、C1。
在步骤S33中，将云名片头像和联系人头像的人脸图像进行比对。
对比人脸图像A1与人脸图像C1。
在步骤S34中，当云名片头像和联系人头像的人脸图像匹配时，确定云名片头像与联系人头像匹配。
当人脸图像A1与人脸图像C1匹配时，可以确定用户A的云名片头像与联系人C的头像匹配。
在步骤S35中，当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
当用户A的云名片的头像与联系人C头像匹配时，可以确定用户A与联系人C同一个人。此时可以将用户A的云名片推荐给用户B，供用户B在联系人C中存储用户A的信息。
本公开实施例提供一种云名片推荐方法。在服务器中获取与联系人头像匹配的第一联系人的云名片，判断云名片头像和联系人头像中提取的人脸图像，确定云名片头像与联系人头像是否匹配，当云名片头像和联系人头像的人脸图像匹配时，可以确定云名片与联系人信息是同一人，将第一联系人的云名片推送给第二联系人。提高了判断云名片头像和联系人头像的人脸图像是否匹配的速度，而且进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
如图4所示，本公开实施例提供了一种云名片推荐方法，具体实施步骤如下：
在步骤S41中，获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括云名片头像，联系人信息中包括联系人头像。
服务器从服务器本地获得用户A的云名片，并从用户B的终端中获取的用户B存储的联系人信息C。
在步骤S42中，对云名片头像和联系人头像进行归一化处理，得到云名片头像和联系人头像的标准头像。
在服务器获得联系人信息时，即可对用户A的云名片的头像及联系人C的头像进行归一化处理，得到用户A的云名片的标准头像D1，联系人C的头像的标准头像D2。
在步骤S43中，对标准头像按照预设规则提取特征点。
在提取特征点时，从标准头像D1与D2提取相同数量的特征点，例如提取的特征点数量为100个，但本公开不以此为限，也可以提取不同数量的特征点。从标准头像D1中提取特征点集1，并且存储提取到的特征点集1；从标准头像D2中提取特征点集2，并且存储提取到的特征点集2。
在步骤S44中，比较云名片头像和联系人头像的标准头像的特征点。
比较特征点集1及特征点集2，并得知特征点集1及特征点集2有78个相同的特征点。
在步骤S45中，根据特征点的比较结果确定云名片头像与联系人头像是否匹配。
根据S44的比较结果特征点集1及特征点集2有78个相同的特征点可以确定用户A的云名片的头像与联系人C的头像匹配。
在步骤S46中，当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
当用户A的云名片的头像与联系人C头像匹配时，可以确定用户A与联系人C是同一个人。此时可以将用户A的云名片推荐给用户B，供用户B在联系人C中存储用户A的信息。
本公开实施例提供了一种云名片推荐方法。在服务器中获取与联系人头像匹配的第一联系人的云名片，比较云名片头像和联系人头像的标准头像的特征点，确定云名片头像与联系人头像是否匹配，可以提高对比云名片头像与联系人头像的速度，还可以提高判断云名片头像与联系人头像是否匹配的准确度，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
如图5所示，本公开实施例提供了一种云名片推荐方法，具体实施步骤如下：
在步骤S51中，获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括名片头像，联系人信息中包括联系人头像。
服务器从服务器本地获得用户A的云名片，并从用户B的终端中获取的用户B存储的联系人信息C。
在步骤S52中，分别从云名片头像和联系人头像中提取人脸图像。
从用户A的云名片的头像中提取人脸图像E1，从联系人C的头像中提取人脸图像E2。
在步骤S53中，对云名片头像和联系人头像的人脸图像进行归一化处理，得到云名片头像和联系人头像的标准人脸图像。
对用户A的云名片的人脸图像E1及联系人C的人脸图像E2进行归一化处理，得到用户A的云名片的标准人脸图像F1，联系人C的头像的标准人脸图像F2。
在步骤S54中，对标准人脸图像按照预设规则提取特征点。
在提取特征点时，从标准人脸图像F1与F2提取相同数量的特征点，提取的特征点数量为100个，但本公开不以此为限，也可以提取不同数量的特征点。从标准人脸图像F1中提取特征点集1，并且存储提取到的特征点集1；从标准人脸图像F2中提取特征点集2，并且存储提取到的特征点集2。
在步骤S55中，比较云名片头像和联系人头像的标准人脸图像的特征点。
比较特征点集1及特征点集2，并得知特征点集1及特征点集2有78个匹配的特征点。
在步骤S56中，根据特征点的比较结果确定云名片头像和联系人头像的人脸图像是否匹配。
根据S55的比较结果特征点集1及特征点集2有78个匹配的特征点可以确定用户A的云名片的头像的人脸图像E1与联系人C的头像的人脸图像E2匹配。
在步骤S57中，当云名片头像和联系人头像的人脸图像匹配时，确定云名片头像与联系人头像匹配。
由于用户A的云名片的头像的人脸图像E1与联系人C的头像的人脸图像E2匹配，则可以确定用户A的云名片的头像与联系人C的头像匹配。
在步骤S58中，当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
当用户A的云名片的头像与联系人C头像匹配时，可以确定用户A与联系人C同一个人。此时可以将用户A的云名片推荐给用户B，供用户B在联系人C中存储用户A的信息。
本公开实施例提供了一种云名片推荐方法。在服务器中获取与联系人头像匹配的第一联 系人的云名片，通过判断云名片头像和联系人头像中提取的人脸图像，并比较云名片头像和联系人头像的标准人脸图像的特征点，确定云名片头像与联系人头像是否匹配，当云名片头像和联系人头像的人脸图像匹配时，可以确定云名片与联系人信息是同一人，提高对比云名片头像与联系人头像的速度，还可以提高判断云名片头像与联系人头像是否匹配的准确度，进一步提高了将云名片推荐给第二联系人时云名片与联系人信息匹配的准确性。
在一实施例中，所述云名片头像与联系人头像匹配例如包括云名片头像与联系人头像中包含相同的人脸；或云名片头像与联系人头像是相同的图像；或云名片头像与联系人头像之间的相似度超过一定阈值。
图6是根据一示例性实施例示出的一种云名片推荐装置的框图。如图6所示，该装置包括：
获取模块61被配置为获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括云名片头像，联系人信息中包括联系人头像：
对比模块62被配置为将云名片头像和联系人头像进行比对：
推荐模块63被配置为当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
如图7所示，对比模块62，包括：
第一计算子模块71被配置为计算云名片头像和联系人头像的第一相似度。
第一确定子模块72被配置为当第一相似度达到预设相似度时，确定云名片头像与联系人头像匹配。
如图8所示，对比模块62，包括：
提取子模块81被配置为分别从云名片头像和联系人头像中提取人脸图像。
对比子模块82被配置为将云名片头像和联系人头像的人脸图像进行比对。
第二确定子模块83被配置为当云名片头像和联系人头像的人脸图像匹配时，确定云名片头像与联系人头像匹配。
如图9所示，对比模块62还包括：
第三确定子模块91被配置为当第一相似度达到预设相似度时，确定云名片头像或联系人头像在服务器存储的所有云名片头像和联系人头像中的出现次数。
第四确定子模块92被配置为当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配。
如图10所示，对比模块还包括：
第二计算子模块101被配置为当出现次数大于预设次数时，根据出现次数和第一相似度计算云名片头像和联系人头像的第二相似度，出现次数越高，第二相似度相对于第一相似度越小：
判断子模块102被配置为判断第二相似度是否达到预设相似度。
第五确定子模块103被配置为当第二相似度未达到预设相似度时，确定云名片头像与联系人头像不匹配。
如图11所示，该装置还包括：
第一处理模块111被配置为对云名片头像和联系人头像进行归一化处理，得到云名片头像和联系人头像的标准头像：
第一提取模块112被配置为对标准头像按照预设规则提取特征点。
对比模块62，包括：
比较子模块113被配置为比较云名片头像和联系人头像的标准头像的特征点：
第六确定子模块114被配置为根据特征点的比较结果确定云名片头像与联系人头像是否匹配。
如图12所示，该装置还包括：
第二处理模块121被配置为对云名片头像和联系人头像的人脸图像进行归一化处理，得到云名片头像和联系人头像的标准人脸图像。
第二提取模块122被配置为对标准人脸图像按照预设规则提取特征点。
对比子模块82还被配置为比较云名片头像和联系人头像的标准人脸图像的特征点；根据特征点的比较结果确定云名片头像和联系人头像的人脸图像是否匹配。
关于上述实施例中的装置，其中各个模块执行操作的具体方式已经在有关该方法的实施例中进行了详细描述，此处将不做详细阐述说明。
图13是根据一示例性实施例示出的一种用于云名片推荐的装置1300的框图。例如，装置1300可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等。
如图13所示，装置1300可以包括以下一个或多个组件：处理组件1302，存储器1304，电源组件1306，多媒体组件1308，音频组件1310，输入/输出(I/O)的接口1312，传感器组件1314，以及通信组件1316。
处理组件1302通常控制装置1300的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件1302可以包括一个或多个处理器1320来执行指 令，以完成上述的方法的全部或部分步骤。此外，处理组件1302可以包括一个或多个模块，便于处理组件1302和其他组件之间的交互。例如，处理组件1302可以包括多媒体模块，以方便多媒体组件1308和处理组件1302之间的交互。
存储器1304被配置为存储各种类型的数据以支持在设备1300的操作。这些数据的示例包括用于在装置1300上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器1304可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件1306为装置1300的各种组件提供电力。电源组件1306可以包括电源管理系统，一个或多个电源，及其他与为装置1300生成、管理和分配电力相关联的组件。
多媒体组件1308包括在该装置1300和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件1308包括一个前置摄像头和/或后置摄像头。当设备1300处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件1310被配置为输出和/或输入音频信号。例如，音频组件1310包括一个麦克风(MIC)，当装置1300处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器1304或经由通信组件1316发送。在一些实施例中，音频组件1310还包括一个扬声器，用于输出音频信号。
I/O接口1312为处理组件1302和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件1314包括一个或多个传感器，用于为装置1300提供各个方面的状态评估。例如，传感器组件1314可以检测到设备1300的打开/关闭状态，组件的相对定位，例如组件为装置1300的显示器和小键盘，传感器组件1314还可以检测装置1300或装置1300一个组件的位置改变，用户与装置1300接触的存在或不存在，装置1300方位或加速/减速和装 置1300的温度变化。传感器组件1314可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件1314还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件1314还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件1316被配置为便于装置1300和其他设备之间有线或无线方式的通信。装置1300可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件1316经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，通信组件1316还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，装置1300可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包括指令的存储器1304，上述指令可由装置1300的处理器1320执行以完成上述方法。例如，非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。
一种非临时性计算机可读存储介质，当存储介质中的指令由移动终端的处理器执行时，使得移动终端能够执行一种云名片推荐方法，该方法包括：
获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括云名片头像，联系人信息中包括联系人头像；
将云名片头像和联系人头像进行比对；
当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
云名片头像和联系人头像进行比对，包括：
计算云名片头像和联系人头像的第一相似度；
当第一相似度达到预设相似度时，确定云名片头像与联系人头像匹配。
将云名片头像和联系人头像进行比对，包括：
分别从云名片头像和联系人头像中提取人脸图像；
将云名片头像和联系人头像的人脸图像进行比对；
当云名片头像和联系人头像的人脸图像匹配时，确定云名片头像与联系人头像匹配。
当第一相似度达到预设相似度时，将云名片头像和联系人头像进行比对还包括：
确定云名片头像或联系人头像在服务器存储的所有云名片头像和联系人头像中的出现次数；
当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配。
当出现次数大于预设次数时，将云名片头像和联系人头像进行比对还包括：
根据出现次数和第一相似度计算云名片头像和联系人头像的第二相似度，出现次数越高，第二相似度相对于第一相似度越小；
判断第二相似度是否达到预设相似度；
当第二相似度未达到预设相似度时，确定云名片头像与联系人头像不匹配。
该方法还包括：
对云名片头像和联系人头像进行归一化处理，得到云名片头像和联系人头像的标准头像；
对标准头像按照预设规则提取特征点；
将云名片头像和联系人头像进行比对，包括：
比较云名片头像和联系人头像的标准头像的特征点；
根据特征点的比较结果确定云名片头像与联系人头像是否匹配。
该方法还包括：
对云名片头像和联系人头像的人脸图像进行归一化处理，得到云名片头像和联系人头像的标准人脸图像；
对标准人脸图像按照预设规则提取特征点；
将云名片头像和联系人头像的人脸图像进行比对，包括：
比较云名片头像和联系人头像的标准人脸图像的特征点；
根据特征点的比较结果确定云名片头像和联系人头像的人脸图像是否匹配。
图14是根据一示例性实施例示出的一种用于云名片推荐的装置1400的框图。例如，装置1400可以被提供为一服务器。参照图14，装置1400包括处理组件1422，其进一步包括一个或多个处理器，以及由存储器1432所代表的存储器资源，用于存储可由处理组件1422的执行的指令，例如应用程序。存储器1432中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1422被配置为执行指令，以执行下述方法：
获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，云名片中包括名片头像，联系人信息中包括联系人头像；
将云名片头像和联系人头像进行比对；
当云名片头像与联系人头像匹配时，将第一联系人的云名片推荐给第二联系人。
将云名片头像和联系人头像进行比对，包括：
计算云名片头像和联系人头像的第一相似度；
当第一相似度达到预设相似度时，确定云名片头像与联系人头像匹配。
将云名片头像和联系人头像进行比对，包括：
分别从云名片头像和联系人头像中提取人脸图像；
将云名片头像和联系人头像的人脸图像进行比对；
当云名片头像和联系人头像的人脸图像匹配时，确定云名片头像与联系人头像匹配。
当第一相似度达到预设相似度时，将云名片头像和联系人头像进行比对还包括：
确定云名片头像或联系人头像在服务器存储的所有云名片头像和联系人头像中的出现次数；
当出现次数小于或等于预设次数时，确定云名片头像与联系人头像匹配。
当出现次数大于预设次数时，将云名片头像和联系人头像进行比对还包括：
根据出现次数和第一相似度计算云名片头像和联系人头像的第二相似度，出现次数越高，第二相似度相对于第一相似度越小；
判断第二相似度是否达到预设相似度；
当第二相似度未达到预设相似度时，确定云名片头像与联系人头像不匹配。
该方法还包括：
对云名片头像和联系人头像进行归一化处理，得到云名片头像和联系人头像的标准头像；
对标准头像按照预设规则提取特征点；
将云名片头像和联系人头像进行比对，包括：
比较云名片头像和联系人头像的标准头像的特征点；
根据特征点的比较结果确定云名片头像与联系人头像是否匹配。
该方法还包括：
对云名片头像和联系人头像的人脸图像进行归一化处理，得到云名片头像和联系人头像的标准人脸图像；
对标准人脸图像按照预设规则提取特征点；
将云名片头像和联系人头像的人脸图像进行比对，包括：
比较云名片头像和联系人头像的标准人脸图像的特征点；
根据特征点的比较结果确定云名片头像和联系人头像的人脸图像是否匹配。
装置1400还可以包括一个电源组件1426被配置为执行装置1400的电源管理，一个有线或无线网络接口1450被配置为将装置1400连接到网络，和一个输入输出(I/O)接口1458。装置1400可以操作基于存储在存储器1432的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由下面的权利要求指出。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。

Claims (15)

  1. 一种云名片推荐方法，其特征在于，所述方法包括：
    获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括云名片头像，所述联系人信息中包括联系人头像；
    将所述云名片头像和所述联系人头像进行比对；
    当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。
  2. 根据权利要求1所述的方法，其特征在于，将所述云名片头像和所述联系人头像进行比对，包括：
    计算所述云名片头像和所述联系人头像的第一相似度；
    当所述第一相似度达到预设相似度时，确定所述云名片头像与所述联系人头像匹配。
  3. 根据权利要求1或2所述的方法，其特征在于，将所述云名片头像和所述联系人头像进行比对，包括：
    分别从所述云名片头像和所述联系人头像中提取人脸图像；
    将所述云名片头像和所述联系人头像中的人脸图像进行比对；
    当所述云名片头像和所述联系人头像中的人脸图像匹配时，确定所述云名片头像与所述联系人头像匹配。
  4. 根据权利要求2所述的方法，其特征在于，当所述第一相似度达到预设相似度时，将所述云名片头像和所述联系人头像进行比对还包括：
    确定所述云名片头像或所述联系人头像在所述服务器存储的所有云名片头像和联系人头像中的出现次数；
    当所述出现次数小于或等于预设次数时，确定所述云名片头像与所述联系人头像匹配。
  5. 根据权利要求4所述的方法，其特征在于，还包括：
    当所述出现次数大于预设次数时，根据所述出现次数和所述第一相似度计算所述云名片头像和所述联系人头像的第二相似度，所述出现次数越高，所述第二相似度相对于所述第一相似度越小；
    判断所述第二相似度是否达到所述预设相似度；
    当所述第二相似度未达到所述预设相似度时，确定所述云名片头像与所述联系人头像不 匹配。
  6. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    对所述云名片头像和所述联系人头像进行归一化处理，得到所述云名片头像和所述联系人头像的标准头像；
    对所述标准头像按照预设规则提取特征点；
    所述将所述云名片头像和所述联系人头像进行比对，包括：
    比较所述云名片头像和所述联系人头像的标准头像的特征点；
    根据特征点的比较结果确定所述云名片头像与所述联系人头像是否匹配。
  7. 根据权利要求3所述的方法，其特征在于，所述方法还包括：
    对所述云名片头像和所述联系人头像的人脸图像进行归一化处理，得到所述云名片头像和所述联系人头像的标准人脸图像；
    对所述标准人脸图像按照预设规则提取特征点；
    所述将所述云名片头像和所述联系人头像的人脸图像进行比对，包括：
    比较所述云名片头像和所述联系人头像的标准人脸图像的特征点；
    根据特征点的比较结果确定所述云名片头像和所述联系人头像的人脸图像是否匹配。
  8. 一种云名片推荐装置，其特征在于，所述装置包括：
    获取模块，用于获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括云名片头像，所述联系人信息中包括联系人头像；
    对比模块，用于将所述云名片头像和所述联系人头像进行比对；
    推荐模块，用于当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。
  9. 根据权利要求8所述的装置，其特征在于，对比模块，包括：
    第一计算子模块，用于计算所述云名片头像和所述联系人头像的第一相似度；
    第一确定子模块，用于当所述第一相似度达到预设相似度时，确定所述云名片头像与所述联系人头像匹配。
  10. 根据权利要求8或9所述的装置，其特征在于，对比模块，包括：
    提取子模块，用于分别从所述云名片头像和所述联系人头像中提取人脸图像；
    对比子模块，用于将所述云名片头像和所述联系人头像中的人脸图像进行比对；
    第二确定子模块，用于当所述云名片头像和所述联系人头像中的人脸图像匹配时，确定所述云名片头像与所述联系人头像匹配。
  11. 根据权利要求9所述的装置，其特征在于，对比模块还包括：
    第三确定子模块，用于当所述第一相似度达到预设相似度时，确定所述云名片头像或所述联系人头像在所述服务器存储的所有云名片头像和联系人头像中的出现次数；
    第四确定子模块，用于当所述出现次数小于或等于预设次数时，确定所述云名片头像与所述联系人头像匹配。
  12. 根据权利要求11所述的装置，其特征在于，对比模块还包括：
    第二计算子模块，用于当所述出现次数大于预设次数时，根据所述出现次数和所述第一相似度计算所述云名片头像和所述联系人头像的第二相似度，所述出现次数越高，所述第二相似度相对于所述第一相似度越小；
    判断子模块，用于判断所述第二相似度是否达到所述预设相似度；
    第五确定子模块，用于当所述第二相似度未达到所述预设相似度时，确定所述云名片头像与所述联系人头像不匹配。
  13. 根据权利要求8所述的装置，其特征在于，所述装置还包括：
    第一处理模块，用于对所述云名片头像和所述联系人头像进行归一化处理，得到所述云名片头像和所述联系人头像的标准头像；
    第一提取模块，用于对所述标准头像按照预设规则提取特征点；
    所述对比模块，包括：
    比较子模块，用于比较所述云名片头像和所述联系人头像的标准头像的特征点；
    第六确定子模块，用于根据特征点的比较结果确定所述云名片头像与所述联系人头像是否匹配。
  14. 根据权利要求10所述的装置，其特征在于，所述装置还包括：
    第二处理模块，用于对所述云名片头像和所述联系人头像的人脸图像进行归一化处理，得到所述云名片头像和所述联系人头像的标准人脸图像；
    第二提取模块，用于对所述标准人脸图像按照预设规则提取特征点；
    所述对比子模块，还用于比较所述云名片头像和所述联系人头像的标准人脸图像的特征点；根据特征点的比较结果确定所述云名片头像和所述联系人头像的人脸图像是否匹配。
  15. 一种云名片推荐装置，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为：
    获取第一联系人的云名片，及第二联系人的终端中存储的联系人信息，所述云名片中包括云名片头像，所述联系人信息中包括联系人头像；
    将所述云名片头像和所述联系人头像进行比对；
    当所述云名片头像与所述联系人头像匹配时，将所述第一联系人的云名片推荐给所述第二联系人。
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