A NEW FACE RECOGNITION SYSTEM - Using HMMs Along with SVD Coefficients
Pooya Davari, Hossein Miar Naimi · 2008
Keywords: Face Recognition, Hidden Markov Model, Singular Value Decomposition. Abstract: In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of 5-state HMM used in pervious researches, we used 7-state HMM to cover more details. As another novel point, we used a small number of quantized Singular Value Decomposition (SVD) coefficients as features describing blocks of face images. This makes the system very fast. In order to additional reduction in computational complexity and memory consumption the images are resized to64×64 jpeg format. The system has been examined on the Olivetti Research Laboratory (ORL) face database. The experiments showed a recognition rate of 99%, using half of the images for training. Our system has been evaluated on YALE database too. Using five and six training images, we obtained 97.78% and 100% recognition rates respectively, 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.