Robust human face image principal component feature extraction method and identification apparatus

Abstract

The invention discloses a robust human face image principal component feature extraction method and identification apparatus. The method comprises: by considering low-rank and sparse characteristics of training sample data of a human face image at the same time, directly performing low-rank and L1-norm minimization on a principal component feature embedded through projection, performing encoding to obtain robust projection P with good descriptiveness, directly extracting a low-rank and sparse principal component union feature of the human face image, and finishing image error correction processing; and by utilizing the embedded principal component feature of a training sample of a robust projection model, obtaining a linear multi-class classifier W* for classifying human face test images through an additional classification error minimization problem. When test samples are processed, a union feature of the test samples is extracted by utilizing a linear matrix P and then the test samples are classified by utilizing the classifier W*; and by introducing a thought of low-rank recovery and sparse description, the principal component feature, with better descriptiveness, of the human face image can be obtained by encoding, the noise can be eliminated, and the effect of human face identification is effectively improved.

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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