A Foley-Sammon Face Recognition Method Fused with Matrix and Vector Feature Extraction

Abstract

The invention discloses a Foley-Sammon face recognition method of fusion matrix and vector feature extraction. First, calculate the two-dimensional Foley-Sammon discriminant vector matrix in the horizontal direction of the face image matrix, then calculate the two-dimensional linear discriminant vector matrix in the vertical direction of the face image matrix, and then use the two discriminant vector matrices to realize the two-way identification of the face image. compression. Each compressed face image is drawn into a vector by row and column and fused into a vector data. Extract the discriminative information of the fused vector data. Finally, the nearest neighbor classifier is used to classify the results to achieve accurate recognition of face images. The invention adopts the Foley-Sammon method in the horizontal direction and the two-dimensional linear identification method in the vertical direction to extract the identification information, so that the complementary identification feature information of the face image can be obtained, and the main feature information of the face image can be preserved as much as possible, so that people can The identification information of the face image is more fully extracted, thereby improving the recognition rate of the face image.

Description

technical field [0001] The invention relates to the fields of pattern recognition and artificial intelligence, in particular to a Foley-Sammon face recognition method which combines matrix and vector feature extraction. Background technique [0002] Face recognition is very active in the field of computer pattern recognition research and has been applied in the commercial and security fields. From static matching of photos in controlled formats (e.g. passports, credit cards, driver’s licenses) and mugshots to real-time matching of surveillance video images. There are three main problems in face recognition technology: image segmentation, feature extraction and recognition. Among them, extracting the features of face images is the key to complete the task of face recognition. [0003] At present, there are mainly vector-based feature extraction methods for extracting the features of face images, such as: principal component analysis, linear discriminant analysis method, Fol...

AI Technical Summary

Problems solved by technology

The disadvantage of the vector-based feature extraction method is that the image matrix must first be converted into a high-dimensional vector, resulting in a large amount of time-consuming face recognition processing and small sample problems.

The vector is generally a high-dimensional vector. When processing these high-dimensional vectors with the Foley-Sammon feature extraction method, the amount of calculation is large, and there is still a small sample problem.

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