Robust face image principal component feature extraction method and recognition device

Abstract

The invention discloses a method for extracting robust face image principal component features and a recognition device. By considering the low-rank and sparse characteristics of face image training sample data at the same time, the principal component features embedded in a projection are directly subjected to low-rank sum L1‑ Norm minimization, coding to obtain a descriptive robust projection P, directly extract the joint low-rank and sparse principal component features of face images, and complete image error correction processing at the same time; use the embedding of training samples of robust projection models Principal component features, through an additional classification error minimization problem to obtain a linear multi-class classifier W * , for the classification of face test images; when processing test samples, use the linear matrix P to extract their joint features, and then use the classifier W * Classify; by introducing the ideas of low-rank recovery and sparse description, the principal component features of face images with stronger descriptiveness can be encoded, which can remove noise and effectively improve the effect of face recognition.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for extracting robust features of principal components of human face images and a recognition device. Background technique [0002] In a large number of real-world applications, most real-world data have high-dimensional features, such as human face images or face images. For an image, the pixels in the image constitute the dimension or feature of the image vector sample data, so an image with a larger size will constitute a vector sample data with a high dimensionality. However, in the process of collection, transmission, display, compression and storage of face images, it is easy to form unfavorable useless features, redundant information or noise data. Therefore, how to extract the most descriptive features from high-dimensional face image data and carry out Face image recognition is a problem that those skilled in the art need to solve. [0003] For face i...

AI Technical Summary

Problems solved by technology

Both PCA-L1 and IRPCA can obtain more descriptive robust principal component features, but both only consider the low-rank or sparse characteristics of the data in the principal component feature encoding

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