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Multi Local Feature Selection Using Genetic Algorithm For Face Identification
Dzulkifli Mohamad
Pages - 1 - 10     |    Revised - 15-02-2007     |    Published - 28-02-2007
Volume - 1   Issue - 2    |    Publication Date - August 2007  Table of Contents
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KEYWORDS
Face Recognition, Facial Feature Extraction, Localization, Neural Network, Genetic Algorithm (GA)
ABSTRACT
Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images. The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. Subsequently, these stages were applied to 3065 images from three distinct facial databases, namely ORL, Yale and AR. The experimental results obtained have shown that recognition rates of more than 89% have been achieved as compared to other global-based features and local facial-based feature approaches. The results also revealed that the technique is robust and invariant to translation, orientation, and scaling.
CITED BY (10)  
1 Jabarullah, B. M., & Babu, C. N. K. (2015). BPNN-hippoamy Algorithm for Statistical Features Classification. Indian Journal of Science and Technology, 8(14).
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4 Cenys, A., Gibavicius, D., Goranin, N., & Marozas, L. (2013). Genetic algorithm based palm recognition method for biometric authentication systems. Elektronika ir Elektrotechnika, 19(2), 69-74.
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6 Sun, Y. (2012). Symmetry and Feature Selection in Computer Vision.
7 Parsi, A., Salehi, M., & Doostmohammadi, A. (2012). Swap training: A genetic algorithm based feature selection method applied on face recognition system. World of Computer Science and Information Technology Journal, 125-130.
8 Khalid, N. E. A., Ariff, N. M., Yahya, S., & Noor, N. M. (2011). A review of bio-inspired algorithms as image processing techniques. In Software engineering and computer systems (pp. 660-673). Springer Berlin Heidelberg.
9 Miao, H. (2010). A multi-operator based simulated annealing approach for robot navigation in uncertain environments. International Journal of Computer Science and Security, 4(1), 50-61.
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Mr. Dzulkifli Mohamad
- Malaysia
dzulkifli@utm.my