Volume 4, Issue 1 (April 2008)                   IJEEE 2008, 4(1): 46-57 | Back to browse issues page

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H. Miar-Naimi, P. Davari. A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients. IJEEE. 2008; 4 (1) :46-57
URL: http://ijeee.iust.ac.ir/article-1-36-en.html
Abstract:   (12591 Views)
In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of five-state HMM used in pervious researches, we used 7-state HMM to cover more details. Indeed we add two new face regions, eyebrows and chin, to the model. As another novel point, we used a small number of quantized Singular Values Decomposition (SVD) coefficients as features describing blocks of face images. This makes the system very fast. The system has been evaluated on the Olivetti Research Laboratory (ORL) face database. In order to additional reduction in computational complexity and memory consumption the images are resized to 64×64 jpeg format. Before anything, an order-statistic filter is used as a preprocessing operation. Then a top-down sequence of overlapping sub-image blocks is considered. Using quantized SVD coefficients of these blocks, each face is considered as a numerical sequence that can be easily modeled by HMM. The system has been examined on 400 face images of the Olivetti Research Laboratory (ORL) face database. The experiments showed a recognition rate of 99%, using half of the images for training. The system has been evaluated on 64×64 jpeg resized YALE database too. This database contains 165 face images with 231×195 pgm format. Using five training image, we obtained 97.78% recognition rate where for six training images the recognition rate was 100%, a record in the literature. The proposed method is compared with the best researches in the literature. The results show that the proposed method is the fastest one, having approximately 100% recognition rate.
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Type of Study: Research Paper |
Received: 2008/10/07 | Accepted: 2013/12/30 | Published: 2013/12/30

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