DSPP (Discriminant Sparsity Preserving Projections) method for unconstrained face recognition

Abstract

The invention provides a DSPP (Discriminant Sparsity Preserving Projections) method for unconstrained face recognition. The method comprises the following steps of: (1) when calculating a sample reconstructing relation matrix W, increasing weight coefficients of similar non-neighboring samples by using category labels and intra-class compactness constraints; (2) when calculating a low-dimensionalmapping matrix P, adding a global constraint factor to further reduce the influence of dissimilar false-neighboring samples on a projection matrix to ensure that a low-dimensional manifold essential structure hidden in complex redundant data can be more accurately mined; and (3) realizing low-dimensional linear mapping of high-dimensional sample data. The method provided by the invention has the beneficial effects that for unconstrained face images obtained in the real environment, redundant information in high-dimensional data the DSPP can more accurately eliminated, essential features are extracted and the representation ability is enhanced; and in the meantime, the data dimension is also reduced, the storage space is saved and the reliability and the effectiveness of the face recognition are greatly increased.

Description

technical field [0001] The invention relates to a discriminant sparse-preserving projection method for unconstrained face recognition, which uses face recognition in an unconstrained environment as the application background to conduct low-dimensional mapping research on high-dimensional face data, mainly including sparse representation of samples Intra-class constrained optimization and globally constrained improvement of low-dimensional projected objective functions. Background technique [0002] With the rapid development of the Internet and sensor technology, the facial image data processed by computers is becoming more and more massive and complex. Therefore, it is particularly important to effectively reduce the dimensionality of massive complex face data and dig out useful essential information hidden under the high-dimensional appearance. On the one hand, it can reduce the data dimension, save storage space, and improve the operating efficiency of the system; on the...

AI Technical Summary

Problems solved by technology

However, when dealing with face data acquired in an unconstrained environment, the samples are complex and changeable. As an unsupervised dimensionality reduction method, traditional SPP optimizes the sparse reconstruction weights inaccurately, which affects the accuracy of unconstrained face recognition.

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