Abstract
Face recognition from an image or video sequences is emerging as an active research area with numerous commercial and law enforcement applications. In this paper different Pseudo 2-dimension Hidden Markov Models (HMMs) are introduced for a face recognition showing performances reasonably fast for binary images. The proposed P2-D HMMs are made up of five levels of states, one for each significant facial region in which the input frontal images are sequenced: forehead, eyes, nose, mouth and chin. Each of P2-D HMMs has been trained by coefficients of an artificial neural network used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. All the P2-D HMMs, applied to the input set consisting of the Olivetti Research Laboratory face database combined to others photos, have achieved good rates of recognition and, in particular, the structure 3-6-6-6-3 has achieved a rate of recognition equal to 100%.
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References
Bevilacqua V, Mastronardi G, Pedone A, Romanazzi G and Daleno D (2006). A Hidden Markov Model for recognition using artificial neural networks, LNCS 4113. Springer, Heidelberg, 126–134
Cottrell GW, Munro P, Zipser D (1987) Learning internal representations from grey scale image: an example of extensional programming. In: Ninth annual conference of the cognitive science society, pp 462–473
Eickeler S, Mller S, Rigoll G (1998) High performance face recognition using pseudo 2-D Hidden Markov Models. In: European control conference
Eickeler S, Mller S and Rigoll G (2000). A recognition of jpeg compressed face images based on statistical methods. Image Vision Comput 18: 279–287
Huang J, Yuen PC, Lai JH and Li C (2004). Face recognition using local and global features. EURASIP J Appl Signal Process 4: 530–541
Kohir V, Desai U (1998) Face recognition using DCTHMM approach, workshop on advances in facial. In: Workshop on advances in facial image analysis and recognition technology (AFIART), Freiburg, Germany
Nefian AV, Monson H (1998a) Hidden Markov Models for face recognition. In: IEEE international conference on acoustics, speech and signal processing (ICASSP98), Seattle, USA, pp 2721–2724
Nefian AV, Monson H (1998b) Detection and recognition using Hidden Markov Models. In: International conference on image processing, Chicago, IL, USA, pp 141–145
Nefian A (1999) A Hidden Markov Model-based approach for face detection and recognition. Ph.D. thesis, Georgia Institute of Technology, Atlanta
Rabiner L and Huang B (1993). Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs
Samaria F and Young S (1994). Face HMM based architecture for face identification. Image Comput Vision 12: 537–583
Samaria F (1992) Face identification using Hidden Markov Model. In: 1st year report Cambridge University Engineering Department, London, UK
Samaria F (1994) Face recognition using Hidden Markov Models. Master’s thesis, University of Cambridge, UK
Yin B, Bai X, Shi Q and Sun Y (2005). Enhanced fisherface for face recognition. J Inform Comput Sci 2: 591–595
Young S, Evermann G, Hain T, Kershaw D, Moore G, Odell J, Ollason D, Povey D, Valtchev V, Woodland P (2002) The HTK Book, Cambridge University Engineering Department, Cambridge, UK
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Bevilacqua, V., Cariello, L., Carro, G. et al. A face recognition system based on Pseudo 2D HMM applied to neural network coefficients. Soft Comput 12, 615–621 (2008). https://doi.org/10.1007/s00500-007-0253-0
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DOI: https://doi.org/10.1007/s00500-007-0253-0