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

1. L0-NORM SPARSE REPRESENTATION BASED
ON MODIFIED GENETIC ALGORITHM FOR
FACE RECOGNITION
Presented By
Sunawar Khan
Mehwish Shabbir
Presented To:
Dr. Ayaz Hussain
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2. RESEARCH PAPER - INTRODUCTION
 Paper Title
L0-norm sparse representation based on modified genetic
algorithm for face recognition.
 Author Name
Zizhu Fan, Ming Ni, Qi Zhu, Chengli Sun, Lipan Kang
Jiaotong University, Nanchang, China
University of Aeronautics and Astronautics, Nanjing, China
University, Nanchang, China
YEAR OF PUBLISH: 2015.
 JOURNAL
ELSEVIER, ScienceDirect 2

3. KEYWORDS
Keywords:
Sparse representation
Classification
Genetic algorithm
Face recognition
L0-norm minimization
L1-norm minimization
Modified GA (MGA)
Optimization methods
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4. SRC
 Sparse representation based classification (SRC)
 To represent a test sample as a sparse linear
combination of the training samples, and then classify
the test samples based on the representation results.
 Uses L1-norm
 L0-norm minimization is an NP-hard problem
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5. GA
 In theory, the L0-norm minimization can be
effectively solved by the stochastic optimization
methods such as the genetic algorithm (GA).
 Conventional GA method does not exploit the prior
knowledge, e.g., the distance information of the
samples.
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6. CONTRIBUTION
1. GASRC is suitable to be directly applied in the
learning settings in which the data are high-
dimensional and the number of the training data is
small.
2. Second, GA is applied in the sparse
representation based classification for the first
time in pattern recognition community.
3. Third, Compared with the conventional SRC
based on L1-norm, the proposed GASRC
algorithm can achieve better classification
performance, particularly when the data are high-
dimensional and the number of the training
samples is small. 15-6

7. ALGORITHM
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8. EXPERIMENT
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9. EXPERIMENT 2
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10. THE END
END
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