Face recognition in unrestrained surroundings has turn out to be more and more prevalent in numerous applications, namely, intelligent visual surveillance, immigration automated clearance system and identity verification systems. The conventional pipeline of a contemporary face recognition system usually consists of face alignment, face detection, feature classification, and representation. In this paper, the input images for face recognition are subjected to feature extraction using Active Appearance Model (AAM). In addition, Type 2- Fuzzy classifier is adopted for classifying the images. Moreover, the proposed scheme is compared with Neural Network, k-NN (Nearest Neighbor) and Type 1-Fuzzy classifiers and the results are obtained.
Yang Long, Fan Zhu, Ling Shao, Junwei Han, “Face recognition with a small occluded training set using spatial and statistical pooling”, Information Sciences, 31 October 2017.
Yan Liang, Yun Zhang, Xian-Xian Zeng, “Pose-invariant 3D face recognition using half face”, Signal Processing: Image Communication, vol. 57, pp. 84-90, September 2017.
Jie Chen, Vishal M. Patel, Li Liu, Vili Kellokumpu, Rama Chellappa, “Robust local features for remote face recognition”, Image and Vision Computing, vol. 64, pp. 34-46, August 2017.
Jiang-Jing Lv, Xiao-Hu Shao, Jia-Shui Huang, Xiang-Dong Zhou, Xi Zhou, “Data augmentation for face recognition”, Neurocomputing, vol. 230, pp. 184-196, 22 March 2017.
Feilong Cao, Xinshan Feng, Jianwei Zhao, “Sparse representation for robust face recognition by dictionary decomposition”, Journal of Visual Communication and Image Representation, vol. 46, pp. 260-268, July 2017.
Martinez, A. and Benavente, R, “The ar face database”, In CVC Technical Report, pp. 24, 1998.
Phillips, P. J., Moon, H., Rizvi, S. A., and Rauss, P. J, “The feret evaluation methodology for face-recognition algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no.10, pp.1090–1104, 2000.
Lee, K.-C., Ho, J., and Kriegman, D. J. “Acquiring linear subspaces for face recognition under variable lighting”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no.5, pp.684–698, 2005.
Phillips, P. J., Moon, H., Rizvi, S. A., and Rauss, P. J, “The feret evaluation methodology for face-recognition algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.10, pp.1090–1104, 2000.
Klare, B. F. and Jain, A. K,” Heterogeneous face recognition using kernel prototype similarities”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no.6, pp.1410–1422, 2013.
Osadchy, M., Keren, D., and Raviv, D, “Recognition using hybrid classifiers”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no.4, pp.759–771, 2016.
Zhang, P., You, X., Ou, W., Chen, C. P., and Cheung, Y.-m, “Sparse discriminative multi-manifold embedding for one-sample face identification”, Pattern Recognition, vol. 52, pp. 249–259, 2016.
Zhu, M. and Martinez, A. M,” Subclass discriminant analysis “, IEEE Transactions on Pattern Analysis and Machine Intelligence”, vol. 28, no.8, pp.1274–1286, 2006.
Martinez, A. M, “Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class”, IEEE Transactions on Pattern analysis and machine intelligence, vol. 24, no.6, pp.748– 763, 2002a.
Tan, X., Chen, S., Zhou, Z.-H., and Zhang, F, “Recognizing partially occluded, expression variant faces from single training image per person with som and soft k-nn ensemble”,IEEE Transactions on Neural Networks, vol.16, no. 4, pp.875–886, 2005.
Martinez, A. M, “Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class”, IEEE Transactions on Pattern analysis and machine intelligence, vol. 24, no.6, pp.748– 763, 2002b
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, vol. 19, no.7, pp.711–720, 1997.
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y, “Robust face recognition via sparse representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
Sakoe, H. and Chiba, S, “Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics”, Speech, and Signal Processing, vol. 26, no. 1, pp. 43–49, 1978.
Zhenyun Deng, Xiaoshu Zhu, Debo Cheng, Ming Zong, Shichao Zhang, “Efficient kNN classification algorithm for big data”, Neurocomputing, vol. 195, pp. 143-148, 26 June 2016.
Bikesh Kumar Singh, Kesari Verma b, A.S. Thoke, "Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images",Expert Systems With Applications, vol. 66, pp.114–123, 2016.
G.J. Edwards, T.F. Cootes, and C.J. Taylor, "Face Recognition Using Active Appearance Models", Wolfson Image Analysis Unit, 1998.
O. Castillo and P. Melin, Type-2 Fuzzy Logic Systems,SpringerBriefs in Computational Intelligence, 2012
Buy article PDF