Method and Apparatus for Generating Facial Feature Verification Model

• US 20160070956A1
• Filed: 09/01/2015
• Published: 03/10/2016
• Est. Priority Date: 09/05/2014
• Status: Active Grant

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Inventors: Tang, Xiaoou, Lu, Chaochao, Xu, Chunjing
• Granted Patent: US 9,514,356 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06V 10/7715 Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

A method and an apparatus for generating a facial feature verification model. The method includes acquiring N input facial images, performing feature extraction on the N input facial images, to obtain an original feature representation of each facial image, and forming a face sample library, for samples of each person with an independent identity, obtaining an intrinsic representation of each group of face samples in at least two groups of face samples, training a training sample set of the intrinsic representation, to obtain a Bayesian model of the intrinsic representation, and obtaining a facial feature verification model according to a preset model mapping relationship and the Bayesian model of the intrinsic representation. In the method and apparatus for generating a facial feature verification model in the embodiments of the present disclosure, complexity is low and a calculation amount is small.

Claims

1. A method for generating a facial feature verification model, wherein the method comprises:

acquiring N input facial images, wherein the N input facial images correspond to M persons with independent identities, wherein N is an integer greater than 2, and wherein M is an integer greater than 2;

performing feature extraction on the N input facial images to obtain an original feature representation of each facial image;

forming a face sample library according to the original feature representations;

grouping samples corresponding to one person with an independent identity in the face sample library to obtain c groups of face samples, wherein c is an integer greater than or equal to 2;

obtaining a common intrinsic representation of the c groups of face samples for samples of each person with an independent identity according to manifold relevance determination;

obtaining a training sample set of an intrinsic representation according to the common intrinsic representation of the c groups of face samples of the person with an independent identity;

training the training sample set of the intrinsic representation to obtain a Bayesian model of the intrinsic representation; and

obtaining a facial feature verification model according to a preset model mapping relationship and the Bayesian model of the intrinsic representation.

2. The method for generating the facial feature verification model according to claim 1, wherein obtaining the common intrinsic representation of the c groups of face samples according to the manifold relevance determination comprises the same quantity of face samples in each group of the c groups of face samples, and wherein obtaining, according to the preset likelihood function and the manifold relevance determination, the private intrinsic representation of each group of face samples in the c groups of face samples and the common intrinsic representation of the c groups of face samples.

3. The method for generating the facial feature verification model according to claim 1, wherein obtaining the training sample set of the intrinsic representation according to the common intrinsic representation of the c groups of face samples comprises obtaining:

a positive training sample set, wherein one training sample pair in the positive training sample set comes from the same person; and

a negative training sample set according to an obtained common intrinsic representation of the c groups of face samples, wherein one training sample pair in the negative training sample set comes from different persons, and wherein training the training sample set of the intrinsic representation to obtain the Bayesian model of the intrinsic representation comprises training the positive training sample set and the negative training sample set, to obtain the Bayesian model of the intrinsic representation.

4. The method for generating the facial feature verification model according to claim 1, wherein training the training sample set of the intrinsic representation to obtain the Bayesian model of the intrinsic representation comprises training the training sample set of the intrinsic representation according to an expectation-maximization algorithm, to obtain the Bayesian model of the intrinsic representation.

5. The method for generating the facial feature verification model according to claim 1, wherein obtaining the facial feature verification model according to the preset model mapping relationship and the Bayesian model of the intrinsic representation comprises mapping the Bayesian model of the intrinsic representation by means of Gaussian process regression of the preset model mapping relationship, to obtain a Bayesian model of an original feature representation, wherein the Bayesian model of the original feature representation is the facial feature verification model.

6. An apparatus for generating the facial feature verification model, wherein the apparatus comprises:

an acquiring module configured to acquire N input facial images, wherein the N input facial images correspond to M persons with independent identities, wherein N is an integer greater than 2, and wherein M is an integer greater than 2;

a feature extracting module configured to perform feature extraction on the N input facial images to obtain an original feature representation of each facial image;

form a face sample library according to the original feature representations;

a grouping module configured to group samples corresponding to one person with an independent identity in the face sample library to obtain c groups of face samples, wherein c is an integer greater than or equal to 2; and

a module for generating a Bayesian model of an intrinsic representation configured to;

obtain a common intrinsic representation of the c groups of face samples for samples of each person with the independent identity according to manifold relevance determination;

obtain a training sample set of an intrinsic representation according to the common intrinsic representation of the c groups of face samples; and

train the training sample set of the intrinsic representation to obtain the Bayesian model of the intrinsic representation, wherein the module for generating the facial feature verification model configured to obtain the facial feature verification model according to a preset model mapping relationship and the Bayesian model of the intrinsic representation.

7. The apparatus for generating the facial feature verification model according to claim 6, wherein the module for generating the Bayesian model of the intrinsic representation is configured to:

obtain, according to a preset likelihood function and the manifold relevance determination, a private intrinsic representation of each group of face samples in the c groups of face samples and the common intrinsic representation of the c groups of face samples for the samples of each person with an independent identity, wherein a quantity of face samples in each group of the c groups of face samples is the same;

obtain the training sample set of the intrinsic representation according to the common intrinsic representation of the c groups of face samples; and

train the training sample set of the intrinsic representation to obtain the Bayesian model of the intrinsic representation.

8. The apparatus for generating the facial feature verification model according to claim 6, wherein the module for generating the Bayesian model of the intrinsic representation is configured to:

obtain the common intrinsic representation of the c groups of face samples for the samples of each person with an independent identity according to the manifold relevance determination;

obtain a positive training sample set and a negative training sample set according to the obtained common intrinsic representation of the c groups of face samples, wherein one training sample pair in the positive training sample set comes from the same person, and wherein one training sample pair in the negative training sample set comes from different persons; and

train the positive training sample set and the negative training sample set to obtain the Bayesian model of the intrinsic representation.

9. The apparatus for generating the facial feature verification model according to claim 6, wherein the module for generating the Bayesian model of the intrinsic representation is configured to:

obtain the common intrinsic representation of the c groups of face samples for the samples of each person with an independent identity according to the manifold relevance determination;

obtain the training sample set of the intrinsic representation according to the common intrinsic representation of the c groups of face samples; and

train the training sample set of the intrinsic representation according to an expectation-maximization algorithm to obtain the Bayesian model of the intrinsic representation.

10. The apparatus for generating the facial feature verification model according to claim 6, wherein the module for generating the facial feature verification model is configured to map the Bayesian model of the intrinsic representation by means of Gaussian process regression of the preset model mapping relationship to obtain a Bayesian model of an original feature representation, wherein the Bayesian model of the original feature representation is the facial feature verification model.