L0-norm sparse representation based on modified genetic algorithm for face recognition

• GASRC exploits the L0-norm optimization to implement a new SRC based method.
• GASRC is suitable for dealing with high-dimensional and small-scale training set.
• GASRC uses a modified GA to determine the representation set for a test sample.
• GASRC achieves better recognition result than many state-of-the-art methods.

The typical sparse representation for classification (SRC) exploits the training samples to represent the test samples, and classifies the test samples based on the representation results. SRC is essentially an L0-norm minimization problem which can theoretically yield the sparsest representation and lead to the promising classification performance. We know that it is difficult to directly resolve L0-norm minimization problem by applying usual optimization method. To effectively address this problem, we propose the L0-norm based SRC by exploiting a modified genetic algorithm (GA), termed GASRC, in this paper. The basic idea of GASRC is that it modifies the traditional genetic algorithm and then uses the modified GA (MGA) to select a part of the training samples to represent a test sample. Compared with the conventional SRC based on L1-norm optimization, GASRC can achieve better classification performance. Experiments on several popular real-world databases show the good classification effectiveness of our approach.