A face feature extraction method and recognition method based on multi-level semantic features

Abstract

The invention discloses a multilevel semantic feature-based face feature extraction method and recognition method. The method includes the following steps that: 1) organ areas of each image in a facial image set A are divided; 2) bottom-level features of each organ are extracted and clustered; two clusters are extracted from clustering results and are adopted as positive and negative samples, and the positive and negative samples are trained in a paired combination manner such that a classifier set can be obtained, and the results of discrimination which is performed on the bottom-level features by the classifier set are united so as to obtain the middle-level features of the organ; the images in the A are the classified according to tags; any two classifications are selected from classification results of the tags and are adopted as positive and negative samples, and the positive and negative samples are trained in a paired combination manner such that a classifier set can be obtained, and the results of classification and discrimination which are performed on all the middle-level features in the A by the classifier set are united so as to obtain high-level features of the tags; the bottom-level features, the middle-level features and the high-level features are adopted to construct face features of the images; face features Vq are generated for any image q to be searched; and the face features Vq are matched with the face features in the A, and query results are returned. With the multilevel semantic feature-based face feature recognition method and recognition method adopted, recognition accuracy and stability can be improved.

Description

technical field [0001] The invention relates to a face feature extraction method and a recognition method, in particular to a face feature extraction method and a recognition method based on multi-level semantic features, which belong to the technical field of image recognition. Background technique [0002] At present, face recognition detection technology has been widely used in various fields and has become a current research hotspot. Its system" patent literature. [0003] Face representation, that is, extracting feature vectors or feature maps from original natural images that can be analyzed by computer operations, is the most important part of face recognition systems. For example, the application number 201310115471.2 and the name "A method and system for automatic face labeling" first detect faces from the intercepted video, obtain a collection of face pictures, and then filter out a collection of face pictures, and at the same time, obtain the images of adjacent f...

AI Technical Summary

Problems solved by technology

In traditional face representation algorithms, only some simple underlying image features are extracted, such as directional gradients, texture edges, etc. Such traditional algorithms often focus too much on the description of details and lack generalization capabilities, making the generated faces Face features are very sensitive to some changes, such as changes in facial expressions, changes in face orientation, lighting conditions, partial occlusion, etc., which ultimately limit the overall performance of the face recognition system

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