Title :
Low complexity 2-D Hidden Markov Model for face recognition
Author :
Otluman, H. ; Aboulnasr, Tyseer
Author_Institution :
Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada
Abstract :
In this paper, a low complexity 2-D Hidden Markov Model (HMM) Face Recognition (FR) system is introduced to provide a 2-D representation of the statistical features of the facial image, as opposed to the 1-D HMM and the 2-D Pseudo HMM (2-DPHMM) found in the literature. The proposed model is designed to have low complexity when compared to the Markov Random Field based 2-D HMM (MRF 2-D HMM). The model is implemented in the 2-D Discrete Cosine Transform (DCT) compressed domain based on a non-overlapped 8×8 pixel blocks scheme to maintain the compatibility with JPEG. It is shown that the proposed model has considerably lower complexity than the MRF 2-D KMM and 2-D PHMM
Keywords :
Viterbi decoding; computational complexity; discrete cosine transforms; face recognition; hidden Markov models; image representation; 2-D discrete cosine transform compressed domain; 2-D representation; JPEG compatibility; face recognition; facial image statistical features; low complexity 2-D hidden Markov model; modified Viterbi algorithm; nonoverlapped 8×8 pixel blocks scheme; Automatic speech recognition; Discrete cosine transforms; Discrete wavelet transforms; Face recognition; Hidden Markov models; Image recognition; Pixel; Stochastic processes; Strips; Testing;
Conference_Titel :
Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Conference_Location :
Geneva
Print_ISBN :
0-7803-5482-6
DOI :
10.1109/ISCAS.2000.857356
