Increasing Face Recognition Rates Using Novel Classification Algorithms

Serestina Viriri, Brett Lagerwall

Abstract


This paper describes and discusses a set of algorithms which can improve ace recognition rates. These algorithms include adaptive K-Nearest Neighbour, daptive weighted average, reverse weighted average and exponential weighted average. ssentially, the algorithms are extensions to the basic classification algorithm sed in most face recognition research. Whereas the basic classification algorithm elects the subject with the shortest associated distance, the algorithms presented in his paper manipulate and extract information from the set of distances between a est image and the training image set in order to obtain more accurate classifications. he base system to which the algorithms are applied uses the eigenfaces technique or recognition with an adapted Viola and Jones algorithm for face extraction. Most f the algorithms proposed show a consistent improvement over the baseline test.

Keywords


Face Recognition, Eigenfaces, Classification Algorithms, Weighting Algorithms

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References


A.W. Senior, R.M. Bolle (2002); Face Recognition and its Applications: Biometric Solutions or Authentication in an E-World, Kluwer Academic Publishers, 2002.

Lin, Shang-hung (2000), An Introduction to Face Recognition Technology, Informing Science he International Journal of an Emerging Transdiscipline, 3(1): 1-7.

M.A. Turk, A.P. Pentland (1991); IEEE Computer Society Conference on Computer Vision nd Pattern Recognition, Proceedings of Computer Vision and Pattern Recognition, 586-591.

H. Moon, P.J. Phillips (2001);

Computational and performance aspects of PCA-based facerecognition lgorithms, Perception, 30(3): 303-321.
http://dx.doi.org/10.1068/p2896

C. Liu, H. Wechsler (1999), Comparative assessment of Independent Component Analysis, nternational Conference on Audio and Video Based Biometric Person Authentication, VBPA'99, Washington D.C. USA, 22-24.

A.M. Bronstein et al (2003); 3D Face Recognition Without Facial Surface Reconstruction, echnion – Computer Science Department.

Viola et al (2004); Robust Real-Time Face Detection, Int. J. Comput. Vision, 57(2): 137- 54.
http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb

G. Bradski (2000); The OpenCV Library, Dr. Dobb's Journal of Software Tools.

H.A. Rowley et al (1995); Human Face Detection in Visual Scenes, Advances in Neural nformation Processing Systems 8, 875-881.

P.J. Phillips et al (1998); The FERET database and evaluation procedure for face recognition lgorithms, Image and Vision Computing Journal, 16(5), 295-306.

P.J. Phillips et al (2000); The FERET Evaluation Methodology for Face Recognition Algorithms, EEE Trans. Pattern Analysis and Machine Intelligence, 22: 1090-1104.
http://dx.doi.org/10.1109/34.879790

T.A.M. Kevenaar et al (2005), Face Recognition with Renewable and Privacy Preserving inary Templates, Proceedings of the Fourth IEEE Workshop on Automatic Identification dvanced Technologies, 21-26.
http://dx.doi.org/10.1109/AUTOID.2005.24

C. Ling et al (2005), Face recognition based on multi-class mapping of Fisher scores, Pattern ecognition, 38(6): 799-811.

F.S. Samaria and A.C. Harter (1995); Parameterisation of a Stochastic Model for Human ace Identification, Workshop on Applications of Computer Vision.

L. Zhang et al (2011), Sparse Representation or Collaborative Representation: Which helps ace recognition?, Proceedings of the 2011 IEEE International Conference In Computer Vision (ICCV), 471-478.
http://dx.doi.org/10.1109/ICCV.2011.6126277

R. Khaji et al (2013), Collaborative Representation for Face Recognition based on Bilateral iltering, IJCSI International Journal of Computer Science, 397-401.

H. Zheng, J. Xie, Z. Jin (2012); Heteroscedastic Sparse Representation Based Classification or Face Recognition, Neural Processing Letters, 233-244.

J. Wright et al (2009); Robust Face Recognition via Sparse Representation, IEEE Transactions n Pattern Analysis and Machine Intelligence (PAMI), 233-244.

W. Andrew et al (2012); Toward a practical face recognition system: Robust alignment and llumination by sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 372-386.

Z. Pengfei et al (2012), Multi-scale Patch based Collaborative Representation for Face Recognition ith Margin Distribution Optimization, Computer Vision-ECCV 2012, Springer Berlin Heidelberg, 822-835.

B. Mikhail, P. Niyogi (2003); Laplacian Eigenmaps for Dimensionality Reduction and Data epresentation, Neural Computation, 1373-1396.




DOI: https://doi.org/10.15837/ijccc.2016.3.571



Copyright (c) 2017 Serestina Viriri, Brett Lagerwall

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