A face recognition method based on two-way two-dimensional iterative non-correlation discriminant analysis

Abstract

The invention discloses a human face recognition method based on the bi-directionally and two-dimensionally iterative and non-relevant discriminant analysis. Firstly, the image sample of a human face is acquired. After that, within-class scatter matrixes, inter-class scatter matrixes and overall scatter matrixes in the horizontal and vertical directions are respectively calculated. By utilizing the non-relevant discriminant transformation method, a first optimal differential vector and an optimal discriminant vector set in each direction are respectively calculated. Afterwards, the projection and dimensionality reduction of the image is conduced by the two optimal discriminant vector sets at the same time. Finally, the classified calculation is conducted by a nearest neighbor classifier to figure out the recognition rate of the image. According to the technical scheme of the invention, the non-relevant discriminant transformation on two-dimensional images is conducted based on the discrimination information in two directions. In this way, the dimensionality reduction and the discrimination information extraction of two-dimensional images both in the horizontal direction and in the vertical direction can be realized at the same time. Therefore, the correct facial recognition is realized. Both the processing time and the storage space are saved, and the recognition rate is ensured to be high.

Description

technical field

[0001] The invention relates to the field of face recognition technology and pattern recognition, in particular to a face recognition method of bidirectional two-dimensional iterative non-correlation discriminant analysis. Background technique

[0002] Face recognition technology is a kind of biometric recognition technology based on human facial feature information. It has a wide range of fields in mathematics, computer, automation, visualization, electronics, virtual reality, pattern recognition and image processing. Research. It has been widely used in government, finance, military, justice, public security, border inspection, electric power, aerospace, education, factories, medical care and many enterprises and institutions. It is non-mandatory (no need to cooperate with face collection equipment), Non-contact (no direct contact with face acquisition equipment), concurrency (can operate on multiple faces at the same time), etc. are superior to other biom...

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