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Detection System in Human Face using Biometrics

P Priya

Abstract


A person is identified with face. Face recognition is one of biometric methods, to identify given face image using main features of face. Biometrics is a growing technology, which has been widely used in forensics, secured access and prison authentication. A biometric system is fundamentally a pattern recognition system that recognizes a person by determining the secured by using his different biological features i.e. fingerprint, retina-scan, iris scan, hand geometry, and face recognition are highly qualified physiological biometrics; and behavioral characteristic are voice recognition, keystroke-scan, and signature-scan. In this paper we discussed about face recognition system.

Cite this Article
Priya P. Detection System in Human Face using Biometrics. Journal of Artificial Intelligence Research & Advances. 2015; 2(3): 9–16p.


Keywords


Face recognition, biometrics, biometric recognition

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References


Zhao W, Chellappa R, Nandhakumar N. Empirical Performance Analysis of Linear Discriminant Classifiers. Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR). 1998; 164–169p.

Yang J, Frangi AF, Yang J-Y, et al. KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Trans Pattern Anal Mach Intell. 2005; 27(2): 230–244p.

Mika S, Rätsch G, Weston J, et al. Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Trans Pattern Anal Mach Intell. 2003; 25(5): 623–628p.

Wu X-J, Kittler J, Yang J-Y, et al. A New Direct LDA (D-LDA) Algorithm for Feature Extraction in Face Recognition. Proc. International Conf. Pattern Recognition. 2004; 4: 545–548p.

Jian H, Yuen PC, Wen-Sheng C. A Novel Subspace LDA Algorithms for Recognition of Face Images with Illumination and Pose Variations. Proc. Int Conf. Machine Learning and Cybernetics. 2004; 6: 3589–3594p.

Nefian AV. Embedded Bayesian Networks for Face Recognition. Proc. of the IEEE Int Conf. Multimedia and Expo; Lusanne, Switzerland. 26–29 Aug 2002; 2: 133–136p.

Mian Zhou, Hong Wei, Stephen Maybank. Gabor Wavelets and AdaBoost in Feature Selection for Face Recognition. Workshop in Application of Computer Vision. 2006.

Wenchao Zhang, Shiguang Shan, Xilin Chen, et al. Local Gabor Binary Patterns Based on Mutual Information for Face Recognition. Int J Image Graph. 2007.


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