WO2019071483A1

WO2019071483A1 - Dialing method and dialing system for intelligent terminal

Info

Publication number: WO2019071483A1
Application number: PCT/CN2017/105753
Authority: WO
Inventors: 江小坤
Original assignee: 深圳传音通讯有限公司
Priority date: 2017-10-11
Filing date: 2017-10-11
Publication date: 2019-04-18

Abstract

Provided in the present invention are a dialing method and a dialing system for an intelligent terminal, the dialing method comprising the following steps: setting a contact picture and contact information of a contact in an address book; acquiring from an image gallery a contact image corresponding to the contact picture of the contact; linking the contact information of the contact to the contact image; and clicking on the contact image to enter the dialing interface of the contact, and displaying the contact information. The present invention associates the address book of an intelligent terminal with an image gallery, and an intelligent terminal user who is browsing the image gallery may directly click on the image of a contact therein to enter the dialing interface for the contact and display the contact information. The present invention also associates the contact pictures in the address book with the contact images in the image gallery and may refresh the contact pictures in the address book in real time to be the latest contact images in the gallery.

Description

Dialing method and dialing system of intelligent terminal

Technical field

The present invention relates to the field of intelligent terminals, and in particular, to a dialing method and a dialing system for an intelligent terminal.

Background technique

Since the advent of smart terminals, smart terminal manufacturers are constantly improving product design, and more and more unimaginable functions come to us. With the rich functions of smart terminals and the rapid development of mobile Internet, today, China has hundreds of millions of intelligent terminal users, and smart terminals have replaced many of the commonly used electronic devices around us, changing our way of life and the surrounding industries.

The address book generally refers to the use of pen records in daily life, and also has this function in smart terminals such as mobile phones, computers, and smart watches. Today's address book can cover a variety of content, such as: contact's name, phone number, unit phone, mobile phone, fax number, email, QQ, MSN, personal homepage, company, street, zip code, birthday, big post, License plates, bank accounts, club names, hobbies, etc.

The mobile terminal of the intelligent terminal is an application/service for realizing the synchronous update and backup of the address book information by using the Internet or the mobile internet. The smart terminal user can enter the contact picture, mobile phone and/or phone number, email, QQ, MSN, communication address and other address book information on any connected device such as mobile phone, computer, etc., or group, manage and organize the previous information. Update, under the permission of the smart terminal user, the contact can see other contact information in his group, thereby achieving address book sharing, if the contact updates his contact information, the address book of the smart terminal user It will be automatically updated to synchronize the address book and leave the old version of the address book information.

Mobile address book, borrowed from the web2.0 statement, mobile address book can be called address book 2.0. This is the basis of the mobile address book and the core service. The mobile terminal of the smart terminal now supports importing contacts from Gmail, Sina, Sohu, MSN, 163, Yahoo, etc. It also supports batch import via CSV or Excel format. In addition, in the manual addition of contacts, the added items are more flexible, more or less, using AJAX technology, so that smart terminal users feel like operating EXCEL. In terms of contact management, in addition to the commonly used functions, the grouping function is a bigger highlight. If the contact of the smart terminal user is also using the mobile address book service, the system will automatically find out that the smart terminal user and the contact person can choose whether to exchange and maintain synchronization. In other words, if the contacts of the smart terminal users are also using the mobile address book service, then everyone's contact information will always be kept up to date.

In the prior art, the contact information of the address book generally includes many items, such as a name, a phone, an avatar, and the like. If the smart terminal user does not customize the avatar for the contact in the smart terminal, the system will set a default picture as the contact avatar; if the smart terminal user adds a custom avatar to the contact, the customization will be displayed. Avatar. However, in the address book of the smart terminal, most of the contacts do not have a custom avatar, even if there is a system default picture, such that the UI is all displaying the default picture, such display method is flawed, not easy to intuitive Different contacts can be distinguished only by the contact name, and the interface is not beautiful enough. In addition, the steps for customizing the avatar for the contact are also cumbersome, requiring manual manual operation and step-by-step access to different operation interfaces to complete the avatar setting. More importantly, in the current smart terminal, the address book has no association with the library, and the caller ID picture of the contact has no correlation with the contact image in the gallery.

Therefore, the present invention provides a dialing method and a dialing system for an intelligent terminal, setting a contact avatar and contact information of a contact in the address book; and obtaining a contact in the gallery corresponding to the contact avatar of the contact An image; linking the contact information of the contact to the contact image; clicking the contact image to enter a dialing interface of the contact, displaying the contact information. The invention associates the address book of the smart terminal with the library, and the user of the intelligent terminal can directly click the contact image in the gallery to enter the dialing interface of the contact and display the contact information when browsing the library; the invention will be in the address book The contact avatar is associated with the contact image in the gallery, and the contact avatar in the address book can be refreshed in real time as the latest contact image in the gallery.

Summary of the invention

In order to overcome the above technical deficiencies, an object of the present invention is to provide a dialing method and a dialing system for an intelligent terminal.

In an aspect of the invention, a dialing method for an intelligent terminal is disclosed, which includes the following steps:

Opening an address book of the smart terminal, setting a contact avatar and contact information of a contact in the address book;

Opening a library of the smart terminal, and acquiring a contact image corresponding to the contact avatar of the contact in the gallery;

Linking the contact information of the contact to the contact image;

Clicking on the contact image to enter the dialing interface of the contact, and displaying the contact information.

Preferably, after the step of acquiring the contact image of the smart terminal, and acquiring the contact image corresponding to the contact avatar of the contact in the gallery, the method further includes:

Putting a contact image corresponding to the contact avatar of the contact in the gallery into a folder named using the contact name;

Link the folder to the contact's contact avatar.

Preferably, the step of linking the folder to the contact avatar of the contact further comprises:

Obtaining a shooting or storage date of the contact image in the folder;

The contact image whose latest photographing or storage date is the latest is the contact avatar.

Obtaining a face percentage of the contact image in the folder;

The contact image that updates the face ratio is the contact avatar.

Preferably, the step of opening the library of the smart terminal and acquiring the contact image corresponding to the contact avatar of the contact in the gallery includes:

Opening a library of the smart terminal, performing face detection and facial feature extraction on the image in the gallery;

Comparing the extracted facial features with facial features of the contact avatar;

When the extracted facial feature coincides with the facial feature of the contact avatar, it is confirmed that the image in the gallery having the facial feature is a contact image corresponding to the contact avatar.

Another aspect of the present invention discloses a dialing system for an intelligent terminal, the dialing system comprising: an avatar setting module, an image recognition module, an image linking module, and an image dialing module;

The avatar setting module starts an address book of the smart terminal, and sets a contact avatar and contact information of a contact in the address book;

The image recognition module is configured to communicate with the avatar setting module, open a gallery of the smart terminal, and obtain a contact image corresponding to the contact avatar of the contact in the gallery;

The image linking module is communicably connected to the avatar setting module and the image recognition module, and links the contact information of the contact to the contact image;

The image dialing module is in communication with the image linking module, clicks on the contact image, enters a dialing interface of the contact, and displays the contact information.

Preferably, the dialing system further includes: an avatar link module;

The avatar link module includes: a folder creation unit and a folder link unit;

The folder establishing unit is configured to put a contact image corresponding to the contact avatar of the contact in the library into a folder named using the contact name;

The folder linking unit is communicably connected to the folder establishing unit, and links the folder to a contact avatar of the contact.

Preferably, the folder linking unit acquires a shooting or storage date of the contact image in the folder;

Preferably, the folder linking unit acquires a face percentage of the contact image in the folder;

The contact image that updates the face ratio is the contact avatar.

Preferably, the image recognition module includes: a feature extraction unit, a feature comparison unit, and an image confirmation unit;

The feature extraction unit starts a library of the smart terminal, and performs face detection and facial feature extraction on the image in the gallery;

The feature comparison unit is in communication with the feature extraction unit, and compares the extracted facial features with facial features of the contact avatar;

The image confirmation unit is communicably connected to the feature comparison unit, and when the extracted facial feature is consistent with the facial feature of the contact avatar, confirming that the image in the gallery having the facial feature is The contact image corresponding to the contact avatar.

After adopting the above technical solution, compared with the prior art, the following beneficial effects are obtained:

The dialing method and the dialing system provided by the present invention, setting a contact avatar and contact information of a contact in the address book; acquiring a contact image corresponding to the contact avatar of the contact in the gallery; The contact information of the contact is linked to the contact image; clicking the contact image to enter the dialing interface of the contact, and displaying the contact information. The invention associates the address book of the smart terminal with the library, and the user of the intelligent terminal can directly click the contact image in the gallery to enter the dialing interface of the contact and display the contact information when browsing the library; the invention will be in the address book The contact avatar is associated with the contact image in the gallery, and the contact avatar in the address book can be refreshed in real time as the latest or most positive contact image in the gallery.

DRAWINGS

1 is a flow chart showing a dialing method in accordance with a preferred embodiment of the present invention;

2 is a schematic flowchart of a step of associating a gallery image with a contact avatar in the dialing method of FIG. 1;

3 is a schematic flowchart of a step of updating a contact avatar in the dialing method of FIG. 2;

4 is a schematic flowchart of a step of updating a contact avatar in the dialing method of FIG. 2;

FIG. 5 is a schematic flowchart of a step of identifying a gallery image in the dialing method of FIG. 1; FIG.

Figure 6 is a block diagram showing the structure of a dialing system in accordance with a preferred embodiment of the present invention.

Reference numerals: 100-dial system; 11-avatar setting module; 12-image recognition module; 13-image link module;

14-image dialing module; 15-heading link module.

Detailed ways

Advantages of the present invention are further explained below in conjunction with the accompanying drawings and specific embodiments.

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. The following description refers to the same or similar elements in the different figures unless otherwise indicated. The following exemplary implementation The embodiments described in the examples do not represent all embodiments consistent with the invention. Instead, they are merely examples of devices and methods consistent with aspects of the invention as detailed in the appended claims.

In the description of the present invention, unless otherwise specified and limited, it should be noted that the term "connected" should be understood broadly, and may be, for example, a mechanical connection or an electrical connection, or may be internal to the two elements, or may be The direct connection may also be indirectly connected through an intermediate medium. For those skilled in the art, the specific meanings of the above terms may be understood according to specific situations.

In the following description, the use of suffixes such as "module" and "unit" for indicating elements is merely an explanation for facilitating the present invention, and does not have a specific meaning per se. Therefore, "module" and "unit" can be used in combination.

The dialing method and dialing system of the present invention can be applied to smart terminals, and the smart terminals can be in various forms. For example, the smart terminal described in the present invention can include, for example, a mobile phone, a smart phone, a notebook computer, a PDA (Personal Digital Assistant). Mobile terminals such as PAD (tablet), PMP (portable multimedia player), navigation device, smart watch, and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Hereinafter, the present invention will be described assuming that the terminal is a mobile terminal and assuming that the mobile terminal is a smart phone. However, those skilled in the art will appreciate that configurations in accordance with embodiments of the present invention can be applied to fixed type terminals in addition to components that are specifically for mobile purposes. For convenience of description, the embodiments of the present invention are described by taking a smart phone as an example, and other application scenarios may be referred to each other.

Referring to FIG. 1, a dialing method of a smart terminal of the present invention includes the following steps:

S100: Open an address book of the smart terminal, and set a contact avatar and contact information of a contact in the address book;

S200: Open a library of the smart terminal, and obtain a contact image corresponding to the contact avatar of the contact in the gallery;

S300: Link the contact information of the contact to the contact image;

S400: Click the contact image to enter the dialing interface of the contact, and display the contact information.

In the present invention, first, in the address book of the smart terminal, the contact is selected, and a contact is selected as an example, and the contact avatar and contact information of the contact are set in the business card of the contact.

The setting of the contact avatar may be that the smart terminal user selects an image stored in the gallery from the library of the smart terminal as the contact avatar, or the smart terminal user aligns the contact on the spot or the The contact photo takes an image as a contact avatar. In addition, the set contact avatar should include the contact's face and facial features, and the contact's face occupies no less than a certain proportion in the contact avatar, such as not less than 60% of the contact avatar; The contact image as the contact avatar may have a certain shape, size, format, and definition limit. For example, the contact avatar is preferably square, 200×200 or 300×300. The avatar format is bmp, jpeg, jpg, gif, etc.

Contact information, which may include, but is not limited to, contact name, phone number, unit phone, mobile phone, fax number, email, QQ, MSN, personal homepage, company, street, zip code, birthday, big post, license plate, Bank account number, club name, hobby, etc. The contact information can be manually input by the user of the smart terminal, or can be imported synchronously through social software or the like.

Then, open the library of the smart terminal, and select an image corresponding to the contact avatar of the contact in the gallery as the contact image.

Referring to FIG. 5, in a preferred embodiment, S200: opening a gallery of the smart terminal, and acquiring a contact image corresponding to the contact avatar of the contact in the gallery includes:

S210: Open a library of the smart terminal, perform face detection and facial feature extraction on the image in the library;

S220: comparing the extracted facial features with facial features of the contact avatar;

S230: When the extracted facial feature corresponds to the facial feature of the contact avatar, confirm that the image in the gallery having the facial feature is a contact image corresponding to the contact avatar.

Specifically, the matching process of the image in the gallery and the contact avatar mainly includes four components: face detection, face image preprocessing, face image feature extraction, and matching and recognition.

- Face detection: In practice, face detection is mainly used for pre-processing of face recognition, that is, the position and size of the face are accurately calibrated in the image. The pattern features contained in the face image are very rich, such as histogram features, color features, template features, structural features, and Haar features. Face detection is to pick out the useful information and use these features to achieve face detection.

-Adaboost algorithm is used in the face detection process to select some rectangular features (weak classifiers) that can represent the face. The weak classifier is constructed as a strong classifier according to the weighted voting method, and then some strong classifications are obtained. The devices are connected in series to form a cascaded classifier of the cascade structure, which effectively improves the detection speed of the classifier.

- Face Image Preprocessing: Image preprocessing for faces is based on face detection results, processing the images and ultimately serving the feature extraction process. The original image acquired by the system is often not directly used due to various conditions and random interference. It must be pre-processed with grayscale correction and noise filtering in the early stage of image processing. For face images, the preprocessing process mainly includes ray compensation, gradation transformation, histogram equalization, normalization, geometric correction, filtering and sharpening of face images.

- Face image feature extraction: The features that can be used are generally classified into visual features, pixel statistical features, face image transform coefficient features, face image algebra features, and the like. Face feature extraction is performed on certain features of the face. Face feature extraction, also known as face representation, is a process of character modeling a face. Method of face feature extraction There are two broad categories: one is based on knowledge representation methods; the other is based on algebraic features or statistical learning.

The knowledge-based representation method mainly obtains the feature data which is helpful for face classification according to the shape description of the face organs and the distance characteristics between them. The feature components usually include the Euclidean distance, curvature and angle between the feature points. . The human face is composed of parts such as eyes, nose, mouth, chin, etc. The geometric description of these parts and the structural relationship between them can be used as important features for recognizing human faces. These features are called geometric features. Knowledge-based face representation mainly includes geometric feature-based methods and template matching methods.

- Face image matching and recognition: The feature data of the extracted face image is searched and matched with the feature template stored in the database. By setting a threshold, when the similarity exceeds the threshold, the result of the matching is output. Face recognition is to compare the face features to be recognized with the obtained face feature templates, and judge the identity information of the faces according to the degree of similarity. This process is divided into two categories: one is confirmation, one-to-one image comparison process, and the other is recognition, which is a one-to-many image matching process.

When implemented, it can be implemented by the following algorithm:

1. Geometric feature based approach

It is precisely because the face is composed of portrait elements such as eyes, nose, mouth, and chin. Because of the differences in the shape, size, and structure of these portrait elements, each face in the world varies widely, so the shape of these portrait elements and The geometric description of the structural relationship can be used as an important feature of face recognition. The geometric feature was first used for the description and recognition of the side profile of the face. First, several significant points were determined according to the side profile curve, and a set of feature metrics such as distance, angle, etc. for identification were derived from these significant points.

The use of geometric features for frontal face recognition is generally performed by extracting the location of important feature points such as the human eye, mouth, nose, and the geometry of important organs such as the eye as classification features. The deformable templating method can be regarded as an improvement of the geometric feature method. The basic idea is to design an organ model with adjustable parameters (ie, deformable template), define an energy function, and minimize the energy function by adjusting the model parameters. The model parameters at this time serve as the geometric features of the organ.

There are two problems with this method. First, the weighting coefficients of various costs in the energy function can only be determined by experience, which is difficult to generalize. Second, the energy function optimization process is very time consuming and difficult to apply. Parameter-based face representation can achieve an efficient description of the salient features of the face, but it requires a lot of pre-processing and fine parameter selection. At the same time, the general geometric features only describe the basic shape and structure relationship of the components, ignoring the local fine features, resulting in the loss of part of the information, more suitable for rough classification, and the existing feature point detection technology in the accuracy rate Far from meeting the requirements, the amount of calculation is also large.

2. Local Feature Analysis (Local Face Analysis)

The representation of the principal subspace is compact, the feature dimension is greatly reduced, but it is non-localized, the support of the kernel function is extended in the entire coordinate space, and it is non-topological, the point adjacent to an axis projection. It has nothing to do with the proximity of points in the original image space. Locality and topologicality are ideal characteristics for pattern analysis and segmentation. It seems that this is more in line with the mechanism of neural information processing. Therefore, it is very important to find expressions with such characteristics.

3. Feature Face Method (Eigenface or PCA)

The feature face method is one of the most popular algorithms proposed by Turk and Pentland in the early 1990s. It has simple and effective features, also called face recognition method based on principal component analysis (PCA).

The basic idea of the feature face face technology is to find the face image of the face image set covariance matrix from the statistical point of view, and to approximate the face image. These feature vectors are called Eigenfaces.

In fact, the eigenface reflects the information that is implicit in the set of face samples and the structural relationship of the face. The feature vectors of the sample set covariance matrix of the eyes, cheeks, and lower jaws are called feature eyes, feature jaws, and feature lips, collectively referred to as feature face faces. The feature face generates a subspace in the corresponding image space, called a child face space. The projection distance of the test image window in the sub-face space is calculated, and if the window image satisfies the threshold comparison condition, it is determined to be a human face.

The method based on feature analysis, that is, the relative ratio of the face reference point and other shape parameters or class parameters describing the facial face feature are combined to form the recognition feature vector, and the overall face-based recognition not only retains the face portion The topological relationship between the pieces, and also the information of each component itself, and the component-based recognition is to design a specific recognition algorithm by extracting the local contour information and the gray information. The method first determines the size, position, distance and other attributes of the facial iris, nose, mouth angle and the like, and then calculates their geometric feature quantities, and these feature quantities form a feature vector describing the image. The core of the technology is actually "local body feature analysis" and "graphic/neural recognition algorithm." This algorithm is a method that utilizes various organs and features of the human face. For example, the corresponding geometric relationship multi-data formation identification parameter is compared, judged and confirmed with all the original parameters in the database. On the basis of the traditional feature face, the researchers noticed that the feature vector with large feature value (ie, feature face) is not necessarily the direction of good classification performance, and accordingly, various feature (subspace) selection methods, such as Peng's, have been developed. The double subspace method, Weng's linear ambiguity analysis method, Belhumeur's FisherFace method, and so on. In fact, the eigenface method is an explicit principal component analysis face modeling. Some linear self-association and linear compression BP networks are implicit principal component analysis methods. They all represent faces as some vectors. Weighted sums, these vectors are the main eigenvectors of the training set cross product matrix. In summary, the eigenface method is a simple, fast and practical algorithm based on transform coefficient features, but because it essentially depends on the gray correlation of the training set and the test set image, and requires the test image to be compared with the training set. So it has a lot of limitations.

The feature face recognition method based on KL transform is an optimal orthogonal transform in image compression. It is used for statistical feature extraction, which forms the basis of subspace method pattern recognition. If KL transform is used For face recognition, it is assumed that the face is in a low-dimensional linear space, and different faces are separable. Since the high-dimensional image space KL transform can obtain a new set of orthogonal bases, the partial orthogonal basis can be preserved. To generate low-dimensional face space, and the basis of low-dimensional space is obtained by analyzing the statistical characteristics of the face training sample set. The generation matrix of the KL transform can be the overall scatter matrix of the training sample set, or it can be a training sample. The inter-class scatter matrix of the set can be trained by using the average of several images of the same person, so that the interference of light and the like can be eliminated to some extent, and the calculation amount is also reduced, and the recognition rate is not decreased.

4. Elastic model based approach

A dynamic link model (DLA) is proposed for object recognition with distortion invariance. The object is described by sparse graphs. The vertices are marked by multi-scale description of the local energy spectrum, and the edges represent topological connections and are marked by geometric distance. Plastic pattern matching techniques are applied to find the most recent known patterns. Modeling the face image (I)(x,y) as a deformable 3D mesh surface (x,y,I(x,y)), transforming the face matching problem into an elastic matching problem of deformable surfaces . The surface deformation is performed by the method of finite element analysis, and it is judged whether the two pictures are the same person according to the deformation condition. This method is characterized by placing the space (x, y) and the gray scale I (x, y) in a 3D space and considering it. Experiments show that the recognition result is significantly better than the feature face method.

The face is encoded into 83 model parameters by automatically locating the salient features of the face, and the face recognition based on the shape information is performed by the method of discrimination analysis. Elastic image matching technology is a recognition algorithm based on geometric features and wavelet texture analysis for gray distribution information. Because the algorithm makes good use of face structure and gray distribution information, it also has automatic and precise positioning. The function of the facial feature points has a good recognition effect, and the adaptive recognition rate is high.

5. Neural Network Method (Neural Networks)

Artificial neural network is a nonlinear dynamic system with good self-organization and self-adaptation ability. At present, the research of neural network methods in face recognition is in the ascendant. First, extract 50 principals of the face, then map it to the 5-dimensional space with the autocorrelation neural network, and then use a common multi-layer perceptron to discriminate, which is better for some simple test images; A hybrid neural network for face recognition, in which unsupervised neural networks are used for feature extraction and supervised neural networks are used for classification. The application of neural network methods in face recognition has certain advantages over the above-mentioned methods, because it is quite difficult to explicitly describe many rules or rules of face recognition, and the neural network method can be learned. The process obtains implicit expressions of these laws and rules, and it is more adaptable and generally easier to implement. Therefore, artificial neural network recognition is fast, but the recognition rate is low. The neural network method usually needs to input the face as a one-dimensional vector, so the input node is huge, and its recognition is important. One goal is to reduce dimensionality.

6. Other methods:

In addition to the above methods, there are several other ideas and methods for face recognition, including the following:

1) Hidden Markov Model

2) Gabor wavelet transform + graphic matching

(1) Accurate extraction of facial feature points and Gabor engine-based matching algorithm has good accuracy and can eliminate changes due to facial gestures, expressions, hairstyles, glasses, lighting environment, and the like.

(2) The Gabor filter limits the Gaussian network function to the shape of a plane wave, and has a preference for the orientation and frequency in the filter design, which is characterized by sensitivity to line edge responses.

(3) However, the recognition speed of the algorithm is very slow, and it is only suitable for playback recognition of video data, and the adaptability to the scene is very poor.

3) Face equal density line analysis matching method

(1) Multiple template matching method

The method is to store a number of standard face image templates or face image organ templates in the library. When performing the comparison, the sample face image is matched with all the pixels in the library using normalized correlation metrics.

(2) Linear Discriminant Analysis (LDA)

(3) eigenface method

The eigenface method treats the image as a matrix, and calculates the eigenvalues and the corresponding eigenvectors as algebraic features for recognition. It has the advantage of not having to extract geometric features such as the nose and mouth, but the recognition rate is not high in a single sample, and When the number of face patterns is large, the amount of calculation is large.

(4) Specific face subspace (FSS) algorithm

This technique is derived from, but essentially different from, the traditional eigenface face recognition method. In the feature face method, all people share a face subspace, and the method creates a face subspace that is private to the individual face for each individual face, thereby not only better describing the difference between different individual faces. And, to the greatest extent, it discards the intra-class differences and noises that are unfavorable for recognition, and thus has better discriminating ability than the traditional feature face algorithm. In addition, for the face recognition problem of only a single training sample for each individual to be identified, a technique for generating multiple training samples based on a single sample is proposed, so that the individual face subspace method requiring multiple training samples can be applied to the single Training sample face recognition problem.

(5) Singular value decomposition (SVD)

It is an effective algebraic feature extraction method. Since singular value features are stable in describing images and have important properties such as transposition invariance, rotation invariance, displacement invariance, and image transformation invariance, singular value features Can be described as an effective algebraic feature of an image. Singular value decomposition technology has been widely used in image data compression, signal processing and pattern analysis.

In the above, the contact image corresponding to the contact avatar in the gallery is identified, and the contact information of the contact is linked to the contact image, so that the smart terminal user clicks on the contact image when browsing the gallery image. , the user can enter the dialing interface of the contact to display the contact information.

Referring to FIG. 2, in a preferred embodiment, S200: after the step of: opening a gallery of the smart terminal, and acquiring a contact image corresponding to the contact avatar of the contact in the gallery, the method further includes:

Link the folder to the contact's contact avatar.

Further, referring to FIG. 3, in a preferred embodiment, the step of linking the folder to the contact avatar of the contact further includes:

Obtaining a shooting or storage date of the contact image in the folder;

Or, referring to FIG. 4, in a preferred embodiment, the step of linking the folder to the contact avatar of the contact further includes:

Obtaining a face percentage of the contact image in the folder;

The contact image that updates the face ratio is the contact avatar.

In the above embodiment, the contact image corresponding to the contact avatar identified in the gallery is placed in a folder, the folder is named using the name of the contact, and the folder is associated with the contact avatar. By editing the avatar, the user can select an image as the contact avatar in the folder in the gallery, and at the same time, can set the latest or most positive image in the folder as the contact avatar in real time.

Referring to FIG. 6, the present invention further provides a dialing system 100 for an intelligent terminal, the dialing system 100 includes: an avatar setting module 11, an image recognizing module 12, an image linking module 13, and an image dialing module 14;

The avatar setting module 11 is configured to open an address book of the smart terminal, and set a contact avatar and contact information of a contact in the address book;

The image recognition module 12 is connected to the avatar setting module 11 to open a gallery of the smart terminal, and obtain a contact image corresponding to the contact avatar of the contact in the gallery;

The image linking module 13 is communicably connected to the avatar setting module 11 and the image recognition module 12, and links the contact information of the contact to the contact image;

The image dialing module 14 is communicably connected to the image linking module 13 and clicks on the contact image to enter Entering the dialing interface of the contact, displaying the contact information.

In a preferred embodiment, the dialing system 100 further includes: an avatar link module 15;

The avatar link module 15 includes: a folder creation unit and a folder link unit;

In a preferred embodiment, the folder linking unit acquires a shooting or storage date of the contact image in the folder;

In a preferred embodiment, the folder linking unit acquires a face percentage of the contact image in the folder;

The contact image that updates the face ratio is the contact avatar.

In a preferred embodiment, the image recognition module 12 includes: a feature extraction unit, a feature comparison unit, and an image confirmation unit;

It should be noted that the embodiments of the present invention are preferred embodiments, and are not intended to limit the scope of the present invention. Any one skilled in the art may use the above-disclosed technical contents to change or modify the equivalent embodiments. Any modification or equivalent changes and modifications of the above embodiments in accordance with the technical spirit of the present invention are still within the scope of the technical solutions of the present invention.

Claims

A dialing method for an intelligent terminal, comprising the steps of:

Opening an address book of the smart terminal, setting a contact avatar and contact information of a contact in the address book;

Opening a library of the smart terminal, and acquiring a contact image corresponding to the contact avatar of the contact in the gallery;

Linking the contact information of the contact to the contact image;

Clicking on the contact image to enter the dialing interface of the contact, and displaying the contact information.
A dialing method according to claim 1, wherein

After the step of acquiring the contact image corresponding to the contact avatar of the contact in the gallery, the method further includes:

Putting a contact image corresponding to the contact avatar of the contact in the gallery into a folder named using the contact name;

Link the folder to the contact's contact avatar.
The dialing method according to claim 2, characterized in that

The step of linking the folder to the contact avatar of the contact further includes:

Obtaining a shooting or storage date of the contact image in the folder;

The contact image whose latest photographing or storage date is the latest is the contact avatar.
The dialing method according to claim 2, characterized in that

The step of linking the folder to the contact avatar of the contact further includes:

Obtaining a face percentage of the contact image in the folder;

The contact image that updates the face ratio is the contact avatar.
A dialing method according to claim 1, wherein

The step of acquiring the contact image of the smart terminal and obtaining the contact image corresponding to the contact avatar of the contact in the gallery includes:

Opening a library of the smart terminal, performing face detection and facial feature extraction on the image in the gallery;

Comparing the extracted facial features with facial features of the contact avatar;

When the extracted facial feature coincides with the facial feature of the contact avatar, it is confirmed that the image in the gallery having the facial feature is a contact image corresponding to the contact avatar.
A dialing system for an intelligent terminal, characterized in that

The dialing system includes: an avatar setting module, an image recognition module, an image linking module, and an image dialing module;

The avatar setting module starts an address book of the smart terminal, and sets a contact avatar and contact information of a contact in the address book;

The image recognition module is configured to communicate with the avatar setting module, open a gallery of the smart terminal, and obtain a contact image corresponding to the contact avatar of the contact in the gallery;

The image linking module is communicably connected to the avatar setting module and the image recognition module, and links the contact information of the contact to the contact image;

The image dialing module is in communication with the image linking module, clicks on the contact image, enters a dialing interface of the contact, and displays the contact information.
The dialing method according to claim 6, wherein

The dialing system further includes: an avatar link module;

The avatar link module includes: a folder creation unit and a folder link unit;

The folder establishing unit is configured to put a contact image corresponding to the contact avatar of the contact in the library into a folder named using the contact name;

The folder linking unit is communicably connected to the folder establishing unit, and links the folder to a contact avatar of the contact.
The dialing method according to claim 7, wherein

The folder linking unit acquires a shooting or storage date of the contact image in the folder;

The contact image whose latest photographing or storage date is the latest is the contact avatar.
The dialing method according to claim 7, wherein

The folder linking unit acquires a face proportion of the contact image in the folder;

The contact image that updates the face ratio is the contact avatar.
The dialing method according to claim 6, wherein

The image recognition module includes: a feature extraction unit, a feature comparison unit, and an image confirmation unit;

The feature extraction unit starts a library of the smart terminal, and performs face detection and facial feature extraction on the image in the gallery;

The feature comparison unit is in communication with the feature extraction unit, and compares the extracted facial features with facial features of the contact avatar;

The image confirmation unit is communicatively coupled to the feature comparison unit when the extracted facial features are associated with the image When the facial features of the human avatar are identical, it is confirmed that the image in the gallery having the facial feature is a contact image corresponding to the contact avatar.