Color and singular value feature-based face in-vivo detection method

Abstract

The invention discloses a color and singular value feature-based face in-vivo detection method, and mainly aims at solving the problem, that the existing face authenticity identification technology is complicated in calculation and low in identification rate. The method is realized through the following steps: 1) marking positive and negative samples of a face database, and dividing the samples into a training set and a testing set; 2) segmenting face images in the training set into blocks and extracting the color features and singular value features of the small blocks in the training set in batches; 3) normalizing vectors of the extracted features and sending the normalized vectors into a support vector machine classifier to train so as to obtain a training model; and 4) carrying out feature extraction on data in the testing set and predicting the features by utilizing the training model so as to obtain a classification result. The color and singular value feature-based face in-vivo detection method is capable of improving the classification efficiency and obtaining higher classification result, and can be used for the face authenticity detection in social networks or real life.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a detection method of a human face image, which can be used in fields such as identity authentication and public security. Background technique [0002] With the continuous advancement of biometric identification technology, the application of face images is becoming more and more extensive. In recent years, face recognition unlocking, face attendance machine, face recognition access control and other applications have begun to appear. In some applications that require relatively high security performance, such as access control and security unlocking, higher requirements are put forward for face anti-counterfeiting technology. requirements. Face recognition technology, as an effective identity authentication technology today, not only requires face detection, but also ordinary face recognition. Some lawbreakers that emerged subsequently used face masks, ph...

AI Technical Summary

Problems solved by technology

[0004] 1 Interaction method: This method is to distinguish whether the judged object is a real person or a photo by detecting the movement of the face or head, such as capturing the movements of the detected object blinking, shaking the head, etc., but this algorithm requires the cooperation of the tester's movements. And it is not very effective for video spoofing;

[0005] 2 Optical flow method: detection is based on the comparison of the similarity between the face image and the background image feature value. This algorithm is more intuitive to understand, but the optical flow method is suitable for dynamic analysis of images, and other algorithm assistance is required for static analysis;

[0006] 3 Texture statistics method: detection is based on the difference in texture details between photos or videos and real people. It has a good effect on photo deception and video deception, but the recognition effect is not good when it comes to the recognition of complex scenes;

[0007] 4. Three-dimensional depth detection, which is detected by monitoring the change of the three-dimensional depth curve of the face image. It is better for ordinary face recognition problems, but its algorithm complexity and calculation amount are relatively large

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