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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
Face Recognition, Facial Feature Extraction, Localization, Neural Network, Genetic Algorithm (GA)
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).
2 Gunasekaran, K., Saravanan, D., & Akilan, P. (2014, November). Performance analysis of integrated multimodal biometrics by means of soft computing techniques. In Science Engineering and Management Research (ICSEMR), 2014 International Conference on (pp. 1-7). IEEE.
3 Farsi, H., & Hasheminejad, M. (2014). Fast Automatic Face Recognition from Single Image per Person Using GAW-KNN. Information Systems & Telecommunication, 188.
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.
5 Sun, Y., & Bhanu, B. (2012, November). Multiple local kernel integrated feature selection for image classification. In Pattern Recognition (ICPR), 2012 21st International Conference on (pp. 2230-2233). IEEE.
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.
10 Pourreza Shahri, R., & Pourreza, H. R. (2009). Offline signature verification using local radon transform and support vector machines. International Journal of Image Processing, 3.
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Belhumeur, P.N.; Hespanha, J.P.; and Kriegman, D. J “Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 711-720, 1997
Chellappa, R.; Wilson, C. L.; and Sirohey, S. “Human and Machine Recognition of Faces: A Survey”. Proceedings of the IEEE, 83(5): 705-740, 1995
Giacinto, G.; Roli, F.; and Fumera. “Unsupervised Learning of Neural Network Ensembles for Image Classification”. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 3: 155-159. Como Italy, 2000
Haddadnia, J.; Ahmadi, M.; and Faez,. “A Hybrid Learning RBF Neural Network for Human Face Recognition with Pseudo Zernike Moment Invariant”. In Proceedings of the IEEE 2002 International Joint Conference on Neural Networks, 1: 11-16. Honolulu, USA, 2002
Haddadnia, J.; Ahmadi, M.; and Faez. “An Efficient Method for Recognition of Human Faces using Higher Orders Pseudo Zernike Moment Invariant”. In Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 315-320. Washington, USA. 2002
Haddadnia, J.; Faez, K.; and Ahmadi, M. “An Efficient Human Face Recognition System Using Pseudo Zernike Moment Invariant and Radial Basis Function Neural Network”. International Journal of Pattern Recognition and Artificial Intelligence 17(1): 41-62, 2003
Haddadnia, J.; Faez, K.; and Moallem, P. “Neural Network Based Face Recognition with Moment Invariants”, In Proceedings of the IEEE International Conference on Image Processing, 1: 1018-1021. Thessaloniki Greece, 2001
Ho, T. K.; Hull, J. J.; and Srihari, S. N. “Decision Combination in Multiple Classifier System”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1): 66-75. 1994
Huang, L. C.; and Chen, C. W. “Human Face Feature Extraction for Face Interpretation and Recognition”. In Proceedings of the IEEE International Conference on Pattern Recognition, 204-207. Hague Netherlands, 1996
Jang, J. -S. R. ANFIS: “Adaptive-Network-Based Fuzzy Inference System”. IEEE Transactions Systems, Man and Cybernetics, 23(3): 665-684, 1993
Kittler, J.; Hatef, M.; Duin, R. P. W.; and Matas, J. “On Combining Classifier”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20: 226-239, 1998
Lin, C. H.; and Wu, J. L. “Automatic Facial Feature Extraction by Genetic Algorithms”. IEEE Transactions on Image Processing, 8(6): 834-845, 1999
Tang, K. S.; Man, K. F.; Kwong, S.; and He, Q. “Genetic Algorithms and Their Applications”. IEEE Signal Processing Magazine 13(6): 22-37, 1996
Turk, M.; and Pentland, A. “Face Recognition using Eigenfaces”. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 586-591. Maui, USA, 1991
Yang, M. H.; Kriegman, D. J.; and Ahuja, N. “Detecting Faces in Images: A Survey”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1): 34-58, 2002
Yen, G. G.; and Nithianandan, N. “Facial Feature Extraction Using Genetic Algorithm”. In Proceedings of the IEEE 2002 Congress on Evolutionary Computation, 2: 1895-1900. Honolulu, USA, 2002.
Yingwei, L.; Sundarajan, N.; and Saratchandran, P. “Performance Evaluation of A Sequential Minimal Radial Basis Function (RBF) Neural Network Learning Algorithm”. IEEE Transactions on Neural Network, 9(2): 308-318, 1998
Yokoo, Y.; and Hagiwara, M. “Human Face Detection Method using Genetic Algorithm”. In Proceedings of the IEEE Congress on Evolutionary Computation, 113-118. Nagoya Japan, 1996
Yullie, A. L.; Cohen, D. S.; and Hallinan, P. W. “Feature Extraction from Faces using Deformable Templates”. In Proceeding of the IEEE International Conference on Pattern Recognition, 104-109. San Diego, USA, 1989
Zhou, W. “Verification of the Nonparametric Characteristics of Backpropagation Neural Networks for Image Classification”. IEEE Transactions Geoscience and Remote Sensing, 37(2): 771-779. 1999.
Mr. Dzulkifli Mohamad
- Malaysia