Phase encoding characteristic and multi-metric learning based vague facial image verification method

Abstract

The invention discloses a phase encoding characteristic and multi-metric learning based vague facial image verification method. The phase encoding characteristic and multi-metric learning based vague facial image verification method comprises (1) a training phase, namely, partitioning sampling images and extracting multi-scale primary characteristics of every image block, performing fisher kernel dictionary learning through the above characteristics to generate into partitioning fisher kernel coding characteristics, performing multi-metric matrix learning on the above coding characteristics to generate a plurality of metric matrixes and obtain the metric distance after training samples are performed on multi-metric matrix projection, calculating the average metric distance and variance of positive samples and negative samples to a set and confirming a final classification threshold through a probability calculation formula of Gaussian distribution and (2) a verification phase, namely, partitioning input facial images and extracting multi-scale primary characteristics, generating partitioning fisher kernel coding characteristics, obtaining the final metric distance through the multi-metric matrix and comparing the distance and the threshold to obtain a facial image verification result. The phase encoding characteristic and multi-metric learning based vague facial image verification method has the advantages that the identification rate is high and the universality is strong.

Description

technical field [0001] The invention relates to the fields of computer vision and pattern recognition, in particular to a fuzzy face image verification method based on phase encoding features and multi-metric learning. Background technique [0002] Face recognition and verification technology has been a research hotspot in the field of computer vision and pattern recognition in the past few decades, and it is also widely used in intelligent monitoring, identity verification and other occasions. After decades of development, face recognition and verification technology has a very high accuracy rate in a controlled environment, but in real applications there are many factors that will affect the accuracy of face recognition and verification, image blur and resolution Low is one of the very important influencing factors. [0003] The main reasons for image blur are as follows: 1. When extracting faces from urban surveillance videos for identification and verification, the obta...

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Problems solved by technology

[0005] Aiming at the problem that the existing face recognition and verification methods cannot robustly deal with common blurred and low-resolution face images in the real environment, the present invention proposes a fuzzy face image verification method based on phase encoding features and multi-metric learning , the method can extract compact and descriptive anti-blur features from blurred face images, and combined with the proposed block measurement method, it improves the classification accuracy of the verification algorithm and compresses the data dimension. On the real data, it still has a good recognition accuracy for blurred images

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