Author:
Malhotra Preeti,Kumar Dinesh
Abstract
Abstract
The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform (DCT) and principal component analysis (PCA). In this research, a nature-inspired well-known algorithm, namely cuckoo search, has been implemented for face recognition. Further, a hybrid method consisting of DCT and PCA is applied to extract the various features by which recognition can be made with a high rate of accuracy. To validate the proposed methodology, the results are also compared with the existing methodologies, such as PSO, differential evolution, and GA.
Subject
Artificial Intelligence,Information Systems,Software
Reference68 articles.
1. A new hybrid bee colony optimization approach for robust optimal design and manufacturing;Appl. Soft Comput.,2013
2. Neuro-genetic design optimization framework to support the integrated robust design optimization process in CE;Concurr. Eng. Res. Appl.,2006
3. Application of DCT blocks with principal component analysis for face recognition, in:;International Conference on Signal, Speech and Image Processing,,2005
4. Feature subset selection using differential evolution, in:;International Conference on Neural Information Processing (ICONIP 2008): Advances in Neuro-Information Processing,,2009
5. Face recognition using particle swarm optimization-based selected features;Int. J. Signal Process. Image Process. Pattern Recognit.,2009
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献