CN111738078A - Face recognition method and device - Google Patents
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Abstract
The invention discloses a face recognition method and a face recognition device, wherein the method comprises the following steps: acquiring an image of a person to be identified, and performing face detection on the image to acquire a face front area; judging whether a shielded area exists in the front area of the face; when the front face area of the face does not have a shielded area, carrying out face recognition on the front face area of the face according to a preset all face area feature library to obtain a target face recognition result; when the front face area of the face has a shielded area, carrying out face recognition on a non-shielded area in the front face area of the face according to a preset partial face area feature library to obtain a face recognition result, and outputting prompt information to prompt a person to be recognized to output voice information; acquiring voice information of a person to be recognized, and performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result; determining a target face recognition result according to the face recognition result and the voiceprint recognition result; and outputting a target face recognition result.
Description
Technical Field
The present invention relates to the field of face recognition technology, and more particularly, to a face recognition method and apparatus.
Background
Face recognition is a popular research topic of computer mode recognition and biological feature identification technology, and is widely applied to the aspects of entertainment, information security, law enforcement, monitoring and the like. In general, face recognition mainly refers to a computer technology for identifying human identities in digital images or video images through human face visual information. Compared with fingerprint identification, palm print identification and the like, the face identification has the characteristics of convenience, rapidness, easy acceptance and the like.
The current face recognition scheme mainly extracts features of a face image containing all face features, and then compares the extracted face features with a feature library containing all face features to obtain a recognition result. However, face recognition cannot be performed on a face image in which a part of face features are blocked, such as a face image of a wearer.
Disclosure of Invention
In view of the above problems, the present invention provides a face recognition method and apparatus, which can recognize a face image that covers part of face features, increase the recognition range, and improve the accuracy of face recognition.
According to a first aspect of the embodiments of the present invention, there is provided a face recognition method, including:
acquiring an image of a person to be identified, and performing face detection on the image to acquire a face front area;
judging whether the front face area of the face has a shielded area or not;
when the front face area of the face does not have a shielded area, carrying out face recognition on the front face area of the face according to a preset all face area feature library to obtain a target face recognition result;
when the front face area of the human face has a shielded area, carrying out human face recognition on a non-shielded area in the front face area of the human face according to a preset partial human face area feature library to obtain a human face recognition result, and outputting prompt information to prompt the person to be recognized to output voice information;
acquiring voice information of the person to be recognized, and performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result;
determining a target face recognition result according to the face recognition result and the voiceprint recognition result;
and outputting the target face recognition result.
In one embodiment, preferably, the performing face recognition on the face frontal region according to a preset total face region feature library to obtain a target face recognition result includes:
extracting all face features from the face front region by using a first feature extraction model, and comparing all face features with face features in a feature library of all face regions;
and determining the target face recognition result according to the comparison result.
In one embodiment, preferably, performing face recognition on a non-occluded area in the face front area according to a preset partial face area feature library to obtain a face recognition result, including:
extracting human face features from the unoccluded area of the human face front area by using a second feature extraction model;
comparing the facial features with facial features in a partial facial region feature library;
and determining the face recognition result according to the comparison result.
In one embodiment, preferably, the voice print recognition of the voice information according to a preset voice feature library to obtain a voice print recognition result includes:
extracting the voiceprint characteristics of the person to be recognized from the voice information by using a voiceprint characteristic extraction model;
comparing the voiceprint features with features in the preset voice feature library;
and determining the voiceprint recognition result according to the comparison result.
In one embodiment, preferably, before the step of acquiring an image of a person to be identified, the method further comprises:
collecting sample images of all face areas of each person to construct the preset all face area feature library;
collecting a sample image of a part of face area of each person to construct the preset part of face area feature library;
and collecting the voice characteristics of each person to construct the preset voice characteristic library.
According to a second aspect of the embodiments of the present invention, there is provided a face recognition apparatus, including:
the system comprises an acquisition module, a recognition module and a recognition module, wherein the acquisition module is used for acquiring an image of a person to be recognized, and carrying out face detection on the image to acquire a face front area;
the judging module is used for judging whether the front area of the face has a shielded area or not;
the first recognition module is used for carrying out face recognition on the face front area according to a preset all face area feature library to obtain a target face recognition result when the face front area does not have a shielded area;
the second recognition module is used for carrying out face recognition on a non-blocked area in the face front area according to a preset partial face area feature library to obtain a face recognition result and outputting prompt information to prompt the person to be recognized to output voice information when the face front area has a blocked area;
the third recognition module is used for acquiring the voice information of the person to be recognized and performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result;
the determining module is used for determining a target face recognition result according to the face recognition result and the voiceprint recognition result;
and the output module is used for outputting the target face recognition result.
In one embodiment, preferably, the first identification module includes:
the first extraction unit is used for extracting all face features from the face front area by using a first feature extraction model;
the first comparison unit is used for comparing all the face features with the face features in the whole face region feature library;
and the first determining unit is used for determining the target face recognition result according to the comparison result.
In one embodiment, preferably, the second identification module includes:
the second extraction unit is used for extracting the face features from the unoccluded area of the face front area by using a second feature extraction model;
the second comparison unit is used for comparing the face features with the face features in the partial face region feature library;
and the second determining unit is used for determining the face recognition result according to the comparison result.
In one embodiment, preferably, the third identification module includes:
a third extraction unit, configured to extract voiceprint features of the person to be recognized from the voice information by using a voiceprint feature extraction model;
the third comparison unit is used for comparing the voiceprint features with the features in the preset voice feature library;
and the third determining unit is used for determining the voiceprint recognition result according to the comparison result.
In one embodiment, preferably, the apparatus further comprises:
the first construction module is used for acquiring sample images of all face areas of each person before acquiring the images of the persons to be identified so as to construct the preset all face area feature library;
the second construction module is used for collecting a sample image of a partial face area of each person to construct the preset partial face area feature library;
and the third construction module is used for collecting the voice characteristics of each person so as to construct the preset voice characteristic library.
In the embodiment of the invention, a feature extraction model for shielding partial face features and a corresponding partial face region feature library are constructed, so that a face image shielding partial face features can be identified, the identification range is enlarged, and the accuracy of face identification is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a face recognition method according to an embodiment of the present invention.
Fig. 2 is a flowchart of step S103 in a face recognition method according to an embodiment of the present invention.
Fig. 3 is a flowchart of step S104 in a face recognition method according to an embodiment of the present invention.
Fig. 4 is a flowchart of step S105 in a face recognition method according to an embodiment of the present invention.
Fig. 5 is a flowchart of another face recognition method according to an embodiment of the present invention.
Fig. 6 is a block diagram of a face recognition apparatus according to an embodiment of the present invention.
Fig. 7 is a block diagram of a first recognition module in the face recognition apparatus according to an embodiment of the present invention.
Fig. 8 is a block diagram of a second recognition module in the face recognition apparatus according to an embodiment of the present invention.
Fig. 9 is a block diagram of a third recognition module in the face recognition apparatus according to an embodiment of the present invention.
Fig. 10 is a block diagram of a face recognition apparatus according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a face recognition method according to an embodiment of the present invention, and as shown in fig. 1, the face recognition method includes:
step S101, obtaining an image of a person to be identified, carrying out face detection on the image, and obtaining a face front area.
And step S102, judging whether the front face area of the human face has a shielded area. For example, when a person wears a mask, a scarf, or the like, key facial features such as the mouth, the nose, and the like are occluded, and therefore, there is an occluded area in the face front area.
And S103, when the front face area of the face does not have a shielded area, carrying out face recognition on the front face area of the face according to a preset all face area feature library to obtain a target face recognition result.
And step S104, when the front face area of the face has a shielded area, carrying out face recognition on a non-shielded area in the front face area of the face according to a preset partial face area feature library to obtain a face recognition result, and outputting prompt information to prompt the person to be recognized to output voice information.
And S105, acquiring the voice information of the person to be recognized, and performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result.
And step S106, determining a target face recognition result according to the face recognition result and the voiceprint recognition result.
And S107, outputting the target face recognition result.
In this embodiment, the face recognition processing is performed on the face front region where the occlusion region does not exist and the face front region where the occlusion region exists, specifically, when the face front region does not exist, all face features, such as eyes, eyebrows, forehead, nose, mouth, and the like, are obtained, and the target face recognition result is obtained by comparing all face features with a corresponding preset all face region feature library. When the front face area of the face is shielded, if a user wears a mask, a scarf and the like, part of the face feature area is shielded, at the moment, the face can be identified according to a preset part of face area feature library, voiceprint identification is carried out by combining voiceprint features of the user, and a final target face identification result is determined according to the face identification result and the voiceprint identification result, so that the accuracy of face identification is ensured on the basis of realizing the identification of the shielded face.
Fig. 2 is a flowchart of step S103 in a face recognition method according to an embodiment of the present invention.
In one embodiment, preferably, the step S103 includes:
step S201, extracting all face features from the face front region by using a first feature extraction model, and comparing all face features with face features in a feature library of all face regions. The first feature extraction model may be a feature extraction model obtained by training a sample image including all human face features and a human face recognition model.
And step S202, determining the target face recognition result according to the comparison result.
Fig. 3 is a flowchart of step S104 in a face recognition method according to an embodiment of the present invention.
As shown in fig. 3, in one embodiment, preferably, the step S104 includes:
step S301, extracting the face features from the unoccluded area of the face front area by using a second feature extraction model. The second feature extraction model may be a feature extraction model obtained by training a sample image including a part of the face features and the face recognition model. The first feature extraction model and the second feature extraction model may employ different models. Of course, the first feature extraction model and the second feature extraction model may also be one feature extraction model, that is, a feature model capable of extracting all the face features and extracting part of the face features is obtained by training a sample image including all the face features, a sample image including part of the face features and a face recognition model.
Step S302, comparing the human face features with the human face features in the partial human face region feature library.
And step S303, determining the face recognition result according to the comparison result.
Fig. 4 is a flowchart of step S105 in a face recognition method according to an embodiment of the present invention.
As shown in fig. 4, in one embodiment, preferably, the step S105 includes:
step S401, extracting the voiceprint characteristics of the person to be recognized from the voice information by using a voiceprint characteristic extraction model;
step S402, comparing the voiceprint features with features in the preset voice feature library;
and S403, determining the voiceprint recognition result according to the comparison result.
In the embodiment, in order to ensure the accuracy of the recognition result of the face with the occlusion, the voiceprint recognition is further performed on the face with the occlusion on the basis of the face recognition, so that the final target face recognition result is determined by combining the voiceprint recognition result and the face recognition result.
Fig. 5 is a flowchart of another face recognition method according to an embodiment of the present invention.
As shown in fig. 5, in an embodiment, preferably before step S101, the method further includes:
step S501, collecting sample images of all face areas of each person to construct the preset all face area feature library;
step S502, collecting a sample image of a partial face area of each person to construct the preset partial face area feature library;
step S503, collecting the voice characteristics of each person to construct the preset voice characteristic library.
In this embodiment, a preset whole face region feature library, a preset partial face region feature library and a preset voice feature library can be respectively constructed as required, so as to ensure the recognition rate and accuracy of face recognition.
Fig. 6 is a block diagram of a face recognition apparatus according to an embodiment of the present invention.
As shown in fig. 6, according to a second aspect of the embodiments of the present invention, there is provided a face recognition apparatus, including:
the acquisition module 61 is used for acquiring an image of a person to be identified, and performing face detection on the image to acquire a face front area;
a judging module 62, configured to judge whether a blocked area exists in the front face area of the face;
the first recognition module 63 is configured to, when the front face area of the face does not have a blocked area, perform face recognition on the front face area of the face according to a preset all face area feature library to obtain a target face recognition result;
the second recognition module 64 is configured to, when a shielded area exists in the front face area of the face, perform face recognition on a non-shielded area in the front face area of the face according to a preset partial face area feature library to obtain a face recognition result, and output prompt information to prompt the person to be recognized to output voice information;
the third recognition module 65 is configured to acquire voice information of the person to be recognized, and perform voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result;
a determining module 66, configured to determine a target face recognition result according to the face recognition result and the voiceprint recognition result;
and the output module 67 is used for outputting the target face recognition result.
Fig. 7 is a block diagram of a first recognition module in the face recognition apparatus according to an embodiment of the present invention.
As shown in fig. 7, in one embodiment, preferably, the first identification module 63 includes:
a first extraction unit 71, configured to extract all face features from the face front region by using a first feature extraction model;
a first comparing unit 72, configured to compare all the face features with the face features in the whole face region feature library;
and a first determining unit 73, configured to determine the target face recognition result according to the comparison result.
Fig. 8 is a block diagram of a second recognition module in the face recognition apparatus according to an embodiment of the present invention.
As shown in fig. 8, in one embodiment, preferably, the second identification module 64 includes:
a second extraction unit 81, configured to extract a face feature from an unobstructed area of the face front area by using a second feature extraction model;
a second comparing unit 82, configured to compare the facial features with facial features in a partial facial region feature library;
and a second determining unit 83, configured to determine the face recognition result according to the comparison result.
Fig. 9 is a block diagram of a third recognition module in the face recognition apparatus according to an embodiment of the present invention.
As shown in fig. 9, in one embodiment, preferably, the third identification module 65 includes:
a third extracting unit 91, configured to extract a voiceprint feature of the person to be recognized from the voice information by using a voiceprint feature extraction model;
a third comparing unit 92, configured to compare the voiceprint feature with features in the preset speech feature library;
and a third determining unit 93, configured to determine the voiceprint recognition result according to the comparison result.
Fig. 10 is a block diagram of a face recognition apparatus according to another embodiment of the present invention.
As shown in fig. 10, in one embodiment, preferably, the apparatus further comprises:
a first construction module 1001, configured to collect sample images of all face regions of each person before acquiring an image of a person to be identified, so as to construct the preset all face region feature library;
a second construction module 1002, configured to collect a sample image of a partial face area of each person, so as to construct the preset partial face area feature library;
a third constructing module 1003, configured to collect voice features of each person to construct the preset voice feature library.
In the embodiment of the invention, a feature extraction model for shielding partial face features and a corresponding partial face region feature library are constructed, so that a face image shielding partial face features can be identified, the identification range is enlarged, and the accuracy of face identification is improved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or the like.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by hardware that is instructed to implement by a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
While the portable multifunctional device provided by the present invention has been described in detail, those skilled in the art will appreciate that the various embodiments and applications of the invention can be modified, and that the scope of the invention is not limited by the disclosure of the present invention.
Claims (10)
1. A face recognition method, comprising:
acquiring an image of a person to be identified, and performing face detection on the image to acquire a face front area;
judging whether the front face area of the face has a shielded area or not;
when the front face area of the face does not have a shielded area, carrying out face recognition on the front face area of the face according to a preset all face area feature library to obtain a target face recognition result;
when the front face area of the human face has a shielded area, carrying out human face recognition on a non-shielded area in the front face area of the human face according to a preset partial human face area feature library to obtain a human face recognition result, and outputting prompt information to prompt the person to be recognized to output voice information;
acquiring voice information of the person to be recognized, and performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result;
determining a target face recognition result according to the face recognition result and the voiceprint recognition result;
and outputting the target face recognition result.
2. The face recognition method of claim 1, wherein the face recognition of the face frontal region is performed according to a preset total face region feature library to obtain a target face recognition result, comprising:
extracting all face features from the face front region by using a first feature extraction model, and comparing all face features with face features in a feature library of all face regions;
and determining the target face recognition result according to the comparison result.
3. The method according to claim 1, wherein performing face recognition on the non-occluded area in the face front area according to a preset partial face area feature library to obtain a face recognition result, comprises:
extracting human face features from the unoccluded area of the human face front area by using a second feature extraction model;
comparing the facial features with facial features in a partial facial region feature library;
and determining the face recognition result according to the comparison result.
4. The face recognition method of claim 1, wherein performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result, comprises:
extracting the voiceprint characteristics of the person to be recognized from the voice information by using a voiceprint characteristic extraction model;
comparing the voiceprint features with features in the preset voice feature library;
and determining the voiceprint recognition result according to the comparison result.
5. The face recognition method according to any one of claims 1 to 4, wherein prior to the step of acquiring an image of a person to be recognized, the method further comprises:
collecting sample images of all face areas of each person to construct the preset all face area feature library;
collecting a sample image of a part of face area of each person to construct the preset part of face area feature library;
and collecting the voice characteristics of each person to construct the preset voice characteristic library.
6. A face recognition apparatus, comprising:
the system comprises an acquisition module, a recognition module and a recognition module, wherein the acquisition module is used for acquiring an image of a person to be recognized, and carrying out face detection on the image to acquire a face front area;
the judging module is used for judging whether the front area of the face has a shielded area or not;
the first recognition module is used for carrying out face recognition on the face front area according to a preset all face area feature library to obtain a target face recognition result when the face front area does not have a shielded area;
the second recognition module is used for carrying out face recognition on a non-blocked area in the face front area according to a preset partial face area feature library to obtain a face recognition result and outputting prompt information to prompt the person to be recognized to output voice information when the face front area has a blocked area;
the third recognition module is used for acquiring the voice information of the person to be recognized and performing voiceprint recognition on the voice information according to a preset voice feature library to obtain a voiceprint recognition result;
the determining module is used for determining a target face recognition result according to the face recognition result and the voiceprint recognition result;
and the output module is used for outputting the target face recognition result.
7. The face recognition apparatus of claim 6, wherein the first recognition module comprises:
the first extraction unit is used for extracting all face features from the face front area by using a first feature extraction model;
the first comparison unit is used for comparing all the face features with the face features in the whole face region feature library;
and the first determining unit is used for determining the target face recognition result according to the comparison result.
8. The face recognition apparatus of claim 6, wherein the second recognition module comprises:
the second extraction unit is used for extracting the face features from the unoccluded area of the face front area by using a second feature extraction model;
the second comparison unit is used for comparing the face features with the face features in the partial face region feature library;
and the second determining unit is used for determining the face recognition result according to the comparison result.
9. The face recognition apparatus of claim 6, wherein the third recognition module comprises:
a third extraction unit, configured to extract voiceprint features of the person to be recognized from the voice information by using a voiceprint feature extraction model;
the third comparison unit is used for comparing the voiceprint features with the features in the preset voice feature library;
and the third determining unit is used for determining the voiceprint recognition result according to the comparison result.
10. The face recognition apparatus according to any one of claims 6 to 9, wherein the apparatus further comprises:
the first construction module is used for acquiring sample images of all face areas of each person before acquiring the images of the persons to be identified so as to construct the preset all face area feature library;
the second construction module is used for collecting a sample image of a partial face area of each person to construct the preset partial face area feature library;
and the third construction module is used for collecting the voice characteristics of each person so as to construct the preset voice characteristic library.
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| CN202010425025.1A CN111738078A (en) | 2020-05-19 | 2020-05-19 | Face recognition method and device |
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| CN113065394A (en) * | 2021-02-26 | 2021-07-02 | 青岛海尔科技有限公司 | Method for image recognition of article, electronic device and storage medium |
| WO2021203718A1 (en) * | 2020-04-10 | 2021-10-14 | 嘉楠明芯(北京)科技有限公司 | Method and system for facial recognition |
| CN114511914A (en) * | 2022-04-19 | 2022-05-17 | 亿慧云智能科技（深圳）股份有限公司 | Face recognition method and device and terminal equipment |
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