Cross-age face verify method based on characteristic learning

Abstract

The invention discloses a cross-age face verify method based on characteristic learning; the method comprises the following steps: 1, obtaining to-be compared two face images; 2, using a face characteristic point positioning method to carry out align operation for the two face images; 3, respectively carrying out feature extraction for each image, wherein the extraction method includes the following steps: a, automatically extracting high-level meaning characteristics through a depth convolution nerve network; b, calculating LBP histogram characteristics of the image; c, fusing the characteristics obtained in the a and b steps, and expressing characteristic vectors; 4, using a cosine similarity method to calculate a distance between the characteristic vectors obtained by the step3, and determining whether the two images are from a same person or not. The method firstly uses the depth network to cross-age face verify, and creatively combines the handwork design LBP histogram characteristics with depth network autonomous learning characteristics, thus realizing complementation between high-rise meaning characteristic and lower characteristics, and providing better accuracy.

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...

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

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