{"references": ["Murray, M.P., Drought, A.B., and Kory, R.C , \"Walking pattern of\nmovement\", American Journal Medicine, vol.46, no. 1, pp.290-332,\nJan. 1967.", "Schuldt Christian, Laptev Ivan and Caputo Barbara, \"Recognizing\nHuman Actions: A Local SVM Approach\". In Proceedings ICPR 2004.\nftp://ftp.nada.kth.se/CVAP/users/laptev/icpr04actions.pdf (retrieved on\n28th May 2011).", "J.N.Pato and L..I.Millett, Editor: Biometric Recognition: Challenge and\nopportunities, Research opportunities and the future of biometrics,\nNational Research Council of the national Academics, Copyright\n2010 by the national Academy of Science. All right reserved.", "D. Sanchez and P. Melin, Modular neural network with fuzzy\nintegration and its optimization using genetic algorithms for human\nrecognition based on iris, ear and voice", "J. C. Vazquez, M. Lopez and P. Melin, Real Time Face Identification\nUsing a Neural Network approach, soft comp. for regcogn, based on\nbiometric, SCI 312, pp. 155- 169, @ Springer-Verlag Berlin\nHeidelberg 2010", "F. Gaxiola, P. Melin and M. Lopez, Modular Neural Network for\nPerson Authentication Using Counter Segmentation of the Human Iris\nBiometrics Measurement, Soft Comp. for Revogn, Based of Biometric,\nSCI 312, pp. 137-153, @ Springer-Verlag, Berlin Heidelberg 2010", "S. Berretti, A. Bimbo and P. Pala, 3D face recognaition using\nisogeodesic stripes, IEEE transaction on pattern analysis and machine\nintelligence, vol. 32, no. 12, December 2010", "Human Face Recognition, Advantages and disadvantages of 3D face\nrecognition,http://www.tutorial.freehost7.com/human_face_recognition/\nbiometrics_and_human_biometrics.htm", "A.K. Jain, Next Generation Biometrics, Department of Computer\nScience & Engineering, Michigan State University, Department of\nBrain & Cognitive Engineering, Korea University, December 10, 2009.\n[10] S. Bengio and J. Mariethoz, Biometric Person Authentication IS A\nMultiple Classifier Problem, Google Inc, Mountain View, CA, USA,\nbengio@google.com, IDIAP Research Institute, Martigny, Switzerland,\nmarietho@idiap.ch\n[11] G. Shakhnarovich T. Darrell, On Probabilistic Combination of Face\nand Gait Cues for Identification, Artificial Intelligence Laboratory,\nMassachusetts Institute of Technology, 200 Technology Square,\nCambridge MA 02139, fgregory,trevorg@ai.mit.edu\n[12] M. N. Eshwarappa and M. V. Latte, Bimodal Biometric Person\nAuthentication System Using Speech and Signature Features,\nInternational Journal of Biometrics and Bioinformatics, (IJBB),\nVolume (4): Issue (4).\n[13] B. Son and Y. Lee, Biometric Authentication System Using Reduced\nJoint Feature Vector of Iris and Face, Division of Computer and\nInformation Engineering, Yonsei University, 134 Shinchon-dong,\nSeodaemoongu, Seoul 120-749, Korea,\n{sonjun,yblee}@csai.yonsei.ac.kr. T. Kanade, A. Jain, and N.K. Ratha\n(Eds.): AVBPA 2005, LNCS 3546, pp. 513-522, 2005.@ Springer-\nVerlag Berlin Heidelberg 005)\n[14] N. B. Boodoo, R. Subramanian, Robust Multi-biometric recognition\nUsing Face and Ear Images, (IJCSIS) Imternational Journal of\nComputer Science and Information Security, Vol. 6, No. 2, 2009.\n[15] I. K. Shlizerman, R. Basri, 3D Face Reconstruction from a Single\nImage Using a Single Reference Face Shape, IEEE Transactions on\nPattern Analysis and Machine Intelligence, Vol. 33, No.2 February\n2011.\n[16] Chetty, G. & White, M. (2010). \"Multimedia Sensor Fusion for\nRetrieving Identity in Biometric Access Control Systems\", ACM\nTransaction on Multimedia Computing, Communications and\nApplications (Special Issue on Sensor Fusion), Nov. 2010.\n[17] Chetty, G. & Wagner, M. (2008). Robust face-voice based speaker\nidentity verification using multilevel fusion, Image and Vision\nComputing, 26, 1249-1260.\n[18] S.M.E. Hossain & G. Chetty, \"Next Generation Identity Verification\nBased on Face and Gait Biometric\" International Conference on\nBiomedical Engineering and Technology\" Kuala Lumpur 17-19 June\n2011."]}

In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..