L0-norm sparse representation based on modified genetic algorithm for face recognition |
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| Institution: | 1. Department of Exact Sciences, University of Feira de Santana, Avenida Transnordestina, s/n, Novo Horizonte, Feira de Santana, Bahia 44036-900, Brazil;2. Departamento de Ciência da Computação, Av. Antônio Carlos, 6627 - Prêdio do ICEx Pampulha, Belo Horizonte, Minas Gerais, 31270-901, Brasil;3. RECOD Lab, Institute of Computing, University of Campinas, Av. Albert Einstein, 1251, Campinas, 13083-852, Brazil;1. School of Electronic and Information Engineering, Beihang University, Mailbox 206 Xueyuan Road, Beijing 100191, PR China;2. Department of Mechanics, Polytechnic University of Milan, Via La Masa 34, Milan 20156, Italy |
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| Abstract: | 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. |
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| Keywords: | Sparse representation Classification Genetic algorithm Face recognition Modified GA (MGA) Optimization methods |
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