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A cross-age face verification method based on feature learning

A technology of feature learning and verification methods, applied in the field of face verification, can solve the problems of no semantic information and weak generalization ability, and achieve the effect of reducing complexity and improving accuracy.

Active Publication Date: 2019-02-19
SUZHOU UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

Traditional face verification uses hand-designed features, which are highly targeted, but are generally low-level features, often do not contain semantic information, and the generalization ability is not strong

Method used

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  • A cross-age face verification method based on feature learning
  • A cross-age face verification method based on feature learning
  • A cross-age face verification method based on feature learning

Examples

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Embodiment 1

[0040] A cross-age face verification method based on feature learning, see figure 1 Shown, comprise the following steps: (1) obtain two pieces of human face images to be compared;

[0041] (2) Utilize the method of facial feature point location to carry out alignment operation to two pieces of human face images;

[0042] Under unconstrained environmental conditions, face images are inevitably affected by facial expressions, lighting, or occlusions, and this effect is amplified when the face parts of two images are not sufficiently aligned.

[0043] The purpose of facial feature point positioning is to further determine the position of facial feature points (eyes, eyebrows, nose, mouth, and facial contour) on the basis of face detection. The basic idea of ​​the positioning algorithm is: the combination of the texture features of the face and the position constraints between each feature point. Early facial feature point localization mainly focused on the localization of sever...

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Abstract

The invention discloses a feature learning-based cross-age face verification method, comprising the following steps: (1) acquiring two face images to be compared; The image is aligned; (3) Feature extraction is performed on each image, the method is: ① Automatically extract high-level semantic features through a deep convolutional neural network; ② Calculate the LBP histogram feature of the image; The features are fused and expressed as feature vectors; (4) The cosine similarity method is used to calculate the distance between the feature vectors of the two images obtained in step (3), so as to judge whether the two images are from the same person. The present invention applies the deep network to cross-age face verification for the first time, and at the same time creatively fuses the manually designed LBP histogram features with the self-learning features of the deep network to realize the complementarity of high-level semantic features and low-level features, with better accuracy Rate.

Description

technical field [0001] The invention relates to a face verification method, in particular to a cross-age face verification method, in particular to a cross-age face verification method based on feature learning. Background technique [0002] Face, as the most prominent area to identify a person, is widely used in various occasions for identification. Generally speaking, the face recognition method includes four steps: face image acquisition and detection, face image preprocessing, face image feature extraction, face matching and verification. Usually, some artificially set feature descriptors, such as LBP, SIFT, and Gabor, are used to represent face data, and the cosine distance is used to measure the similarity of a pair of images to achieve judgment verification. [0003] But as we age, our face inevitably changes. In some occasions, it is just a photo of a person of different ages, for example, a photo of only a dozen years ago. It is necessary to compare and verify the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/16G06V40/168G06V40/172
Inventor 王朝晖翟欢欢刘纯平季怡龚声蓉葛瑞
Owner SUZHOU UNIV
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