International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.1, Dec. 2012

Enhanced Face Recognition using Data Fusion

Full Text (PDF, 433KB), PP.98-103

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Alaa Eleyan

Index Terms

Data Fusion;Principal Component Analysis;Discrete Cosine Transform;Local Binary Patterns


In this paper we scrutinize the influence of fusion on the face recognition performance. In pattern recognition task, benefiting from different uncorrelated observations and performing fusion at feature and/or decision levels improves the overall performance. In features fusion approach, we fuse (concatenate) the feature vectors obtained using different feature extractors for the same image. Classification is then performed using different similarity measures. In decisions fusion approach, the fusion is performed at decisions level, where decisions from different algorithms are fused using majority voting. The proposed method was tested using face images having different facial expressions and conditions obtained from ORL and FRAV2D databases. Simulations results show that the performance of both feature and decision fusion approaches outperforms the single performances of the fused algorithms significantly.

Cite This Paper

Alaa Eleyan,"Enhanced Face Recognition using Data Fusion", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.1, pp.98-103, 2013.DOI: 10.5815/ijisa.2013.01.10


[1]W. Zhao, R. Chellappa, A. Rosenfeld and P.J. Phillips, “Face Recognition: A Literature Survey/”. ACM Computing Surveys, pp. 399-458, 2003.

[2]F. Al-Osaimi, M. Bennamoun and A. Mian, “Spatially Optimized Data Level Fusion of Texture and Shape for Face Recognition”. To appear in IEEE Trans. on Image Processing, 2011.

[3]M. Vatsa, R. Singh, A. Noore, and A. Ross, “On the Dynamic Selection of Biometric Fusion Algorithms”. IEEE Transactions on Information Forensics and Security, vol. 5, no. 3, pp. 470-479, 2010.

[4]A. Ross and A. K. Jain, “Fusion Techniques in Multibiometric Systems”, in Face Biometrics for Personal Identification. R. I. Hammound, B. R. Abidi and M. A. Abidi (Eds.), Springer, 2007, pp. 185-212.

[5]A. Ross and A. K. Jain, “Multimodal Human Recognition Systems”, in Multi-Sensor Image Fusion and Its Applications. R.S. Blum and Z. Liu (Eds.), CRC Taylor and Francis, 2006, pp. 289–301.

[6]D.H.P. Salvadeo, N.D.A. Mascarenhas, J. Moreira, A.L.M. Levada, D.C. Corrêa, “Improving Face Recognition Performance Using RBPCA MaxLike and Information Fusion”. Computing in Science & Engineering journal vol. 13 no. 5, pp. 14-21, 2011.

[7]D.R. Kisku, M. Tistarelli, J.K. Sing, and P. Gupta, “Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-Classifier and Multi-Classifier Paradigm”. Computer Vision and Pattern Recognition Workshop, 2009, pp. 60-65. 

[8]D. G. Lowe, “Distinctive Image Features From Scale-Invariant Keypoints”. International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

[9]The Olivetti Database;

[10]Á. Serrano, I. M. de Diego, C. Conde, E. Cabello, L Shen, and L. Bai, “Influence of Wavelet Frequency and Orientation in an SVM Based Parallel Gabor PCA Face Verification System”, H. Yin et al. (Eds.): IDEAL Conference 2007, Springer-Verlag LNCS 4881, pp. 219–228, 2007.

[11]M. Turk, A. Pentland, “Eigenfaces for Recognition”. Journal of Cognitive Neuroscience, vol. 3, pp. 71-86, 1991. 

[12]M. Kirby, and L. Sirovich, “Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces”. IEEE Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103-108, 1990.

[13]Fabrizia M. de S. Matos, Leonardo V. Batista, and JanKees v. d. Poel. 2008. Face Recognition Using DCT Coefficients Selection”. In Proceedings of the 2008 ACM Symposium on Applied computing, 2008, pp. 1753-1757.

[14]Podilchuk, C. Xiaoyu Zhang, “Face Recognition using DCT-Based Feature Vectors”, IEEE International Conference on Acoustics, Speech, and Signal Processing, 1996, vol. 4, pp. 2144-2147.

[15]Z. M. Hafed and M. D. Levine, “Face Recognition Using the Discrete Cosine Transform”. International Journal of Computer Vision, vol. 43 no.3, 2001.

[16]T. Ahonen, A. Hadid, and M. Pietikainen. “Face Recognition with Local Binary Patterns”. In European Conference on Computer Vision, 2004, pp. 469-481.

[17]T. Ojala, M. Pietikainen, T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”. IEEE Transaction on pattern analysis and machine learning, vol. 24, no. 7, pp. 971-987.

[18]V. Perlibakas. “Distance Measures for PCA-Based Face Recognition”. Pattern recognition Letters. vol. 25, no. 6, pp. 711-724, 2004.