Supervising neighborhood preserving embedding face recognition method and system and face recognizer

Abstract

The invention discloses a supervising neighborhood preserving embedding face recognition method and system and a face recognizer. The supervising neighborhood preserving embedding face recognition method comprises the steps that initial dimensionality reduction is conducted on a training sample set, and then a primary dimensionality reduction training sample set and a primary dimensionality reduction training sample matrix are obtained; the class information of each training point in the primary dimensionality reduction training sample set is marked through a class divergence matrix; secondary dimensionality reduction is conducted on the primary dimensionality reduction training sample matrix through a secondary projection matrix, and then a secondary dimensionality reduction training sample matrix and a secondary dimensionality reduction sample set are obtained; a test sample is established, dimensionality reduction is conducted on the test sample twice, and then a secondary dimensionality reduction test sample is obtained; a secondary dimensionality reduction training sample which is closest to the secondary dimensionality reduction test sample is extracted, and the class label of the secondary dimensionality reduction training sample is given to the secondary dimensionality reduction test sample. Compared with the dimensionality reduction method in the prior art, the supervising neighborhood preserving embedding face recognition method can achieve supervised learning, and a high recognition rate is obtained.

Description

technical field [0001] The present application relates to the technical field of face detection, and more specifically, to a supervised neighborhood preserving embedding face recognition method and system and a face recognizer. Background technique [0002] Face recognition is a biometric identification technology based on human facial feature information. It analyzes the face image by computer, extracts effective information from the image and automatically identifies it. Face recognition technology is widely used in security systems and human It has become one of the important research topics in the field of computer vision and pattern recognition. [0003] Generally speaking, face images are stored in high-dimensional data, and the training data set needs to be projected into a low-dimensional space for dimensionality reduction. [0004] Traditional dimensionality reduction algorithms are divided into linear dimensionality reduction and nonlinear dimensionality reduction...

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Problems solved by technology

However, the NPE algorithm does not judge the type of neighborhood when perfo

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