Home   >   CSC-OpenAccess Library   >    Manuscript Information
Feature Fusion and Classifier Ensemble Technique for Robust Face Recognition
Hamayun A. Khan
Pages - 1 - 15     |    Revised - 31-03-2017     |    Published - 30-04-2017
Volume - 11   Issue - 1    |    Publication Date - April 2017  Table of Contents
MORE INFORMATION
KEYWORDS
Face Recognition, Feature Space, Wavelet Analysis, HOG Descriptors, Principal Component Analysis, Feature Fusion, Classifier Space, Classifier Ensemble, Linear Discriminant Classifiers, Robust Classification, Cross Validation.
ABSTRACT
Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.
1 Google Scholar 
2 BibSonomy 
3 Scribd 
4 Eng Paper 
5 SlideShare 
A. K. Jain, A. Ross, and K. Nandakumar. Introduction to Biometrics. US, Springer, 2011.
A. Kar, P. Bhattacharjee, M. Nasipuri, D. K. Basu, M. Kundu. "High performance human face recognition using gabor based pseudo hidden Markov model." Int. J. Appl. Evol. Comput., 2013, pp. 81-102.
B. Amos, B. Ludwiczuk, and M. Satyanarayanan. "OpenFace: A general-purpose face recognition library with mobile applications." Technical report, CMU-CS-16-118, CMU School of Computer Science, 2016.
B. M. Alexander, M. M. Bronstein, R. Kimmel, and A. Spira. "3D face recognition without facial surface reconstruction." Technion-Computer Science Department Technical Report, 2003.
B. Peter, J. P. Hespanha, and D. Kriegman. "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.
B. R. Francis, and M. I. Jordan. "Kernel independent component analysis." Journal of machine learning research, vol. 3, no. 1, pp. 1-48, Jul. 2002.
B. Roberto, and T. Poggio. "Face recognition: Features versus templates." IEEE transactions on pattern analysis and machine intelligence, vol. 15, no. 10, pp. 1042-1052, 1993.
B. Stefano, A. Del Bimbo, and P. Pala. "3D face recognition using isogeodesic stripes." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2162-2177, 2010.
B. Stephen. "Deep learning and face recognition: the state of the art." In Proc. SPIE Defense+ Security, 2015.
B. W. Kevin, K. Chang, and P. Flynn. "A survey of approaches and challenges in 3D and multi-modal 3D+ 2D face recognition." Computer vision and image understanding, vol. 101, no. 1, pp. 1-15, 2006.
G. Friedrich, and Y. Yeshurun. "Seeing people in the dark: face recognition in infrared images." In Proc. International Workshop on Biologically Motivated Computer Vision, 2002, pp. 348-359.
G. Shakhnarovich, and M. Baback, Face recognition in subspaces. In Handbook of Face Recognition, London, Springer, 2011, pp. 19-49.
H. A. Khan, M. Al-Akaidi and R. W. Anwar. "Linear Discriminant Classifier Ensemble for Face Recognition." In Proc. of MESM' 2014.
H. Di, J. Sun, X. Yang, D. Weng, and Y. Wang. "3d face analysis: Advances and perspectives." In Proc Chinese Conference on Biometric Recognition, 2014, pp. 1-21.
H. Di, Y. Wang, and Y. Wang. "A robust method for near infrared face recognition based on extended local binary pattern." In Proc International Symposium on Visual Computing Conference, 2007, pp. 437-446.
H. Heiko. "Kernel PCA for novelty detection." Pattern Recognition, vol. 40, no. 3, pp. 863-874, 2007.
I. Daubechies. "Ten lectures on wavelets." Society for industrial and applied mathematics, 1992.
J. A. Laszlo, H. Hashimoto, and T. Kubota. "Robust Facial Expression Recognition Using Near Infrared Cameras." JACIII, vol. 16, no. 2, pp. 341-348, 2012.
J. S. Kais. "Face recognition system using PCA and DCT in HMM." Int. J. Adv. Res. Comput. Commun. Eng , vol. 4, pp. 13-18, 2015.
L. G. David. "Distinctive image features from scale-invariant keypoints." International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
L. Juwei, K. N. Plataniotis, and A. N. Venetsanopoulos. "Face recognition using kernel direct discriminant analysis algorithms." IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 117-126, 2003.
L. Martin, J. C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Wurtz, and W. Konen. "Distortion invariant object recognition in the dynamic link architecture." IEEE Transactions on computers, vol. 42, no. 3, pp. 300-311, 1993.
L. T. Sing. "Image representation using 2D Gabor wavelets." IEEE Transactions on pattern analysis and machine intelligence, vol. 18, no. 10, pp. 959-971, 1996.
L. Yann, Y. Bengio, and G. Hinton. "Deep learning." Nature, vol. 521, no. 7553, pp. 436-444, 2015.
M. Chihaoui, A. Elkefi, W. Bellil, and C. B. Amar. "A Survey of 2D Face Recognition Techniques." Computers, vol. 5, no. 4, pp. 21, 2016.
M. Chihaoui, W. Bellil, A. Elkefi, and C. B. Amar. "Face recognition using HMM-LBP." Hybrid Intelligent Systems, pp. 249-258, 2016.
M. Stewart, J. R. Movellan, and T. J. Sejnowski. "Face recognition by independent component analysis." IEEE Transactions on neural networks, vol. 13, no. 6, pp. 1450-1464, 2002.
N. Dalal and B. Triggs. "Histograms of oriented gradients for human detection." In Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 886-893.
N. Jiquan, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. "Multimodal deep learning." In Proc. The 28th international conference on machine learning (ICML-11), 2011, pp. 689-696.
N. V. Ara, and M. H. Hayes. "Face detection and recognition using hidden Markov models." In Proc International Conference on Image Processing, 1998, pp. 141-145.
P. M. Omkar, A. Vedaldi, and A. Zisserman. "Deep Face Recognition." BMVC, vol. 1, no. 3, pp. 1-12, 2015.
R. C. Gonzalez, and R. E. Woods. Digital Image Processing, Pearson International Edition, 2008.
R. Jafri and H. R. Arabnia. "A survey of face recognition techniques." Jips, vol. 5, no. 2, pp. 41-68, 2009.
R. Jiang, S. Al-Madeed, A. Bouridane, D. Crookes, and A. Beghdadi, eds. Biometric Security and Privacy: Opportunities & Challenges in The Big Data Era. US, Springer, 2017.
S. Florian, D. Kalenichenko, and J. Philbin. "Facenet: A unified embedding for face recognition and clustering." In Proc. The IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 815-823.
S. Yi, Y. Chen, X. Wang, and X. Tang. "Deep learning face representation by joint identification-verification." Advances in neural information processing systems, pp. 1988-1996, 2014.
T. Ahonen, A. Hadid, and M. Pietikäinen. "Face recognition with local binary patterns." In Proc European conference on computer vision, 2004, pp. 469-481.
T. Matthew, and A. Pentland. "Eigenfaces for recognition." Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
T. Matthew, and A. Pentland. "Face recognition using eigenfaces." In Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
T. Yaniv, M. Yang, M. Ranzato, and L. Wolf. "Deepface: Closing the gap to human-level performance in face verification." In Proc. The IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1701-1708.
The AT & T Database of Faces. Internet: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html [19 February 2017].
The Yale Database. Internet: http://vision.ucsd.edu/content/yale-face-database [23 February 2017].
W. Laurenz, N. Krüger, N. Kuiger, and C. Von Der Malsburg. "Face recognition by elastic bunch graph matching." IEEE Transactions on pattern analysis and machine intelligence, vol. 19, no. 7, pp. 775-779, 1997.
Y. Jian, D. Zhang, A. Frangi, and J. Yang. "Two-dimensional PCA: a new approach to appearance-based face representation and recognition." IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 1, pp. 131-137, 2004.
Y. Ming-Hsuan. "Face recognition using extended isomap." In Proc International Conference on Image Processing, 2002.
Z. Zhou, J. Wu, and W. Tang. "Ensembling neural networks: many could be better than all." Artificial Intelligence, vol. 137, no. 1-2, pp. 239-263, 2002.
Dr. Hamayun A. Khan
Faculty of Computer Studies/ Arab Open University Industrial Ardiya, 13033, Kuwait - Kuwait
hamayun73@yahoo.com