Abstract
In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.
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References
Zheng, S.: CASIA Gait Database collected by Institute of Automation, Chinese Academy of Sciences, CASIA Gait Database, http://www.sinobiometrics.com
Huang, L.: Person Recognition By Feature Fusion, Dept. of Engineering Technology Metropolitan State College of Denver Denver. IEEE, USA (2011)
Bringer, J., Chabanne, H.: Biometric Identification Paradigm Towards Privacy and Confidentiality Protection, Biometric: Theory. In: Nichols, E.R. (ed.) Application and Issues, pp. 123–141 (2011)
Jain, A.K.: Next Generation Biometrics, Department of Computer Science & Engineering. Michigan State University, Department of Brain & Cognitive Engineering, Korea University (2009)
Yampolskiy, R.V., Govindaraja, V.: Taxonomy of Behavioral Biometrics. In: Behavioral Biometrics for Human Identification, pp. 1–43 (2010)
Meraoumia, A., Chitroub, S., Bouridane, A.: Fusion of Finger-Knuckle-Print and Palmprint for an Efficient Multi-biometric System of Person Recognition. In: IEEE Communications Society Subject Matter Experts for Publication in the IEEE ICC (2011)
Berretti, S., Bimbo, A., Pala, P.: 3D face recognaition using isogeodesic stripes. IEEE Transaction on Pattern Analysis and Machine Intelligence 32(12) (2010)
Yuan, L., Mu, Z., Xu, Z.: Using Ear Biometrics for Personal Recognition. School of Information Engineering, Univ. of Science and Technology Beijing, Beijing 100083, yuanli64@hotmail.com
Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)
Jain, A.K., Hong, L., Kulkarni, Y.: A multimodal biometric system using fingerprints, face and speech. In: 2nd Int’l Conf. AVBPA, vol. 10, pp. 182–187 (1999)
Wang, Y., Tan, T., Jain, A.K.: Combining face and iris biometrics for identity verification. In: Int’l Conf. AVBPA, pp. 805–813 (2003)
Chang, K., et al.: Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics. IEEE Trans. PAMI 25, 1160–1165 (2003)
Kittler, J., et al.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 226–239 (1998)
Smith, L.I.: A tutorial on Principal Components Analysis
Linear discriminant analysis, Wikipedia, http://www.wikipedia.org
Multi Layer Perceptron, http://www.neoroph.sourceforge.net
Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research, jplatt@microsoft.com, Technical Report MSR-TR-98-14 (17) (1998)
Shlizerman, I.K., Basri, R.: 3D Face Reconstruction from a Single Image Using a Single Reference Face Shape. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(2) (2011)
Hossain, E., Chetty, G.: Multimodal Identity Verification Based on Learning Face and Gait Cues. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part III. LNCS, vol. 7064, pp. 1–8. Springer, Heidelberg (2011)
Chin, Y.J., Ong, T.S., Teoh, A.B.J., Goh, M.K.O.: Multimodal Biometrics based Bit Extraction Method for Template Security, Faculty of Information Science and Technology, Multimedia University, Malaysia, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. IEEE (2011)
Multilayer Perceptron Neural Networks, The Multilayer Perceptron Neural Network Model, www.dtreg.com
UCMG Gait Database (to be made publicly available), http://www.canberra.edu.au
Hinton, G.E.: To recognize shapes, first learn to generate images. Progress in Brain Research 165, 535–547 (2007)
Huo, X., Ni, X., Smith, A.K.: A survey of manifold-based learning methods. In: Recent Advances in Datamining of Enterprise Data Algorithms and Applications, pp. 691–745 (2004)
LeCun, Y., Chopra, S., Hadsell, R.: A tutorial on energy-based learning. In: Predicting Structured Data. MIT Press (2006)
Kusakunniran, W., Zhang, J., Wu, Q., Li, H.: Multiple Views Gait Recognition using View Transformation Model of Gait Energy Image. In: Second IEEE International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS 2009): a workshop in ICCV 2009, pp. 1058–1064 (2009)
Chetty, G., Wagner, M.: Liveness Detection Using Cross Modal Correlations in Face-Voice Person Authentication. In: Proceedings Interspeech Conference, pp. 2181–2184 (2005)
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Hossain, E., Chetty, G. (2013). Multimodal Feature Learning for Gait Biometric Based Human Identity Recognition. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_89
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DOI: https://doi.org/10.1007/978-3-642-42042-9_89
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