Abstract
A method, named competitive sparse representation classification (CSRC), is proposed for face recognition in this paper. CSRC introduces a lowest competitive deletion mechanism which removes the lowest competitive sample based on the competitive ability of training samples for representing a probe in multiple rounds collaborative linear representation. In other words, in each round of competing, whether a training sample is retained or not in the next round depends on the ability of representing the input probe. Because of the number of training samples used for representing the probe decreases in CSRC, the coding vector is transformed into a low dimensional space comparing with the initial coding vector. Then the sparse representation makes CSRC discriminative for classifying the probe. In addition, due to the fast algorithm, the FR system has less computational cost. To verify the validity of CSRC, we conduct a series of experiments on AR, Extended YB, and ORL databases respectively.
Highlights
Face Recognition (FR) has become to a hot research area for its convenience in daily life
We propose a supervised sparse constraint method named as competitive sparse representation classification (CSRC)
To evaluate the proposed CSRC algorithm, we conduct a serious of experiments on images from AR database, Extended YB database and ORL database respectively, as well as comparing with state-of-the-art methods including Collaborative representation based on Classification (CRC), Sparse Representation Classification (SRC), and TPTSR
Summary
Face Recognition (FR) has become to a hot research area for its convenience in daily life. Linear representation methods are very popular which represent the probe with training samples from gallery set. Collaborative representation (CR) method has achieved good performance for FR [1,2,3,4],in which a given testing image y can be represented by a training set A with a coding vector x, i.e. y=Ax. The training set A including all samples from all subjects is an over-complete dictionary. It is known that face images from a specific class lie in a linear subspace and a probe can be represented by images which have the same label as the probe. The induced sparse constraint on coding vector uses l0 -norm so that the representation problem is formulized as: min || x ||0 s.t. The induced sparse constraint on coding vector uses l0 -norm so that the representation problem is formulized as: min || x ||0 s.t. || y Ax ||2
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