A Comparative Study of Different Dimension Reduction Techniques for Face Recognition
[1]
Ali Khalili Mobarakeh, Department of Mechanical Engineering, University of Málaga, Doctor Ortiz Ramos, Malaga, Spain.
[2]
Juan Antonio Cabrera Carrillo, Department of Mechanical Engineering, University of Málaga, Doctor Ortiz Ramos, Malaga, Spain.
[3]
Juan Jesús Castillo Aguilar, Department of Mechanical Engineering, University of Málaga, Doctor Ortiz Ramos, Malaga, Spain.
[4]
Shadi Mahmoodi Khaniabadi, Department of Electrical and Computer Engineering, University of Oklahoma, Norman, United States.
[5]
Abolfazl Zargari, Department of Electrical and Computer Engineering, University of Oklahoma, Norman, United States.
Smart recognition of human identity is a global concern in our world in order to provide security and safety. In the past three decades, Biometrics, which refers to the unique physiological or behavioral characteristics of human beings, has been successfully employed to distinguish between individuals. Face recognition is one of the effective methods, which is a rapidly evolving technology and has been widely used in many applications such as forensics, secure access, and prison and airport security gates. In this survey, we had gone through some most recent face recognition techniques listing their advantages and disadvantages in order to evaluate their performance on ORL face database.
Biometrics, Face Recognition, Dimensionally Reduction
[1]
Damavandinejadmonfared, S., et al., Finger vein recognition using PCA-based methods. World Academy of Science, Engineering and Technology, 2012. 66: p. 2012.
[2]
Damavandinejadmonfared, S., et al., Evaluate and Determine the Most Appropriate Method to Identify Finger Vein. Procedia Engineering, 2012. 41(0): p. 516-521.
[3]
Mobarakeh, A. K., et al. Applying Weighted K-nearest centroid neighbor as classifier to improve the finger vein recognition performance. in Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on. 2012.
[4]
Mobarakeh, A. K., et al. Finger Vein Recognition Using Local Mean Based K-Nearest Centroid Neighbor Classifier. in Advanced Materials Research. 2013. Trans Tech Publications.
[5]
Ali Khalili Mobarakeh, S. M. K., Saba Nazari, Sina Ashooritootkaboni, Mohsen Pashna, Finger vein recognition based on spares representation classifier. American Journal of Engineering, Technology and Society, 2014. 1(3): p. 15-18.
[6]
Bishop, C. M., Pattern Recognition. Machine Learning, 2006.
[7]
Bowyer, K. W., BIOMETRICS RESEARCH. 2016.
[8]
Hadjikhani, N., et al., Improving emotional face perception in autism with diuretic bumetanide: a proof-of-concept behavioral and functional brain imaging pilot study. Autism, 2015. 19(2): p. 149-157.
[9]
Kim, W., S. Suh, and J.-J. Han, Face Liveness Detection From a Single Image via Diffusion Speed Model. Image Processing, IEEE Transactions on, 2015. 24(8): p. 2456-2465.
[10]
Gou, J. and Z. Yi, Locality-Based Discriminant Neighborhood Embedding. The Computer Journal, 2012.
[11]
Xiaofei He and P. Niyogi, Locality Preserving Projections. Proceedings of Advances in Neural Information Processing Systems, 2003.
[12]
Zhang, W., et al., Discriminant neighborhood embedding for classification. Pattern Recognition, 2006. 39(11): p. 2240-2243.
[13]
The ORL face database. Available at: http://www.cl. cam.ac.uk/research/dtg/attarchive/facedatabase.html.