Under-sample face recognition method based on multi-dimensional scale transformation network and block weighting method

Abstract

The invention discloses an under-sample face recognition method based on a multi-dimensional scale transformation network and a block weighting method. The under-sample face recognition method comprises the following steps: firstly, carrying out block processing on each image in a sample set formed by a single face image to obtain a new sample data set; secondly, learning filter parameters of themulti-dimensional scale transformation network by utilizing the new sample data set, extracting feature expressions of the sample images by utilizing the filter parameters, and constructing a corresponding feature library; thirdly, performing feature extraction on the partitioned test image data set by calling filter parameters, and after the extracted features are synthesized in a weighting mode,performing matching processing on the extracted features and the features in the feature library; and finally, obtaining classification and identification information of the final test face image byusing a matching result. According to the under-sample face recognition method method, the face image features are accurately extracted by using an unsupervised feature extraction network framework, so that the face recognition accuracy is improved, and a solid foundation is laid for public security construction.

Description

technical field [0001] The invention relates to the technical field of deep learning applications, in particular to an under-sample face recognition method based on a multi-dimensional scale transformation network and a block weighting method. Background technique [0002] In recent years, video surveillance has been popularized in large and medium-sized cities across the country, and has been widely used in the construction of social security prevention and control systems, and has become a powerful technical means for public security organs to investigate and solve crimes. Especially in mass incidents, major cases and double robbery cases, evidence clues obtained from video surveillance play a key role in the rapid detection of cases. Due to the impact of shooting time, space and environment, the face images that can be captured are diverse. Facts have proved that using one face image sample per person to identify face images under different lighting conditions, different...

AI Technical Summary

Problems solved by technology

For the under-sampled face recognition problem, the captured face images often have factors such as different lighting and different expressions, and there are too few label data available for training, which seriously affects the accuracy of face recognition.

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