A Video Face Recognition Method Based on Minimum Normalized Distance Learning

Abstract

The invention discloses a video face recognition method based on minimum normalized distance learning, which belongs to the technical field of video face recognition. The present invention constructs the loss function and obtains the gradient, and uses the stochastic gradient descent method to optimize the loss function to obtain the minimum normalized distance metric matrix M. In the process of face recognition, the metric matrix M is used to calculate the distance between each target image and Query the minimum normalized distance between video sequences, and finally get the recognition result, so that only a single image of the target to be recognized is needed as the target image, which is more in line with the real scene, and the distance model obtained after training has a small amount of calculation and can meet Real-time requirements in actual use.

Description

technical field [0001] The invention relates to a video face recognition method based on minimum normalization distance learning, and belongs to the technical field of video face recognition. Background technique [0002] In video face recognition, especially in video surveillance scenarios, there is often a big difference between the image of the person to be recognized and the query image sequence captured in the actual scene. The traditional image-based face recognition method cannot effectively deal with these differences. And cannot effectively use video information, so the effect is not good; [0003] The current popular method is to use the point-to-set distance metric learning method to learn to measure the feature distance between the target image and the query image sequence, but such methods often need to accumulate a certain number of video frames and process these video frames. , in order to measure the feature distance between the static target image and the v...

AI Technical Summary

Problems solved by technology

[0002] In video face recognition, especially in video surveillance scenes, there is often a large difference between the image of the person to be recognized and the query image sequence captured in the actual scene. Traditional image-based face recognition methods cannot effectively deal with these differences. And the video information cannot be effectively used, so the effect is not good;

[0003] The current popular method is to use the point-to-set distance metric learning method to learn to measure the feature distance between the target image and the query image sequence, but such methods often need to accumulate a certain number of video frames and process these video frames , to measure the characteristic distance between the static target image and the video sequence, which takes too long in the test phase, and the recognition effect is easily disturbed by low-quality pictures in the query image sequence, so it cannot be applied to high real-time requirements or recognition In application scenarios with high precision requirements

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