Face identification system using single hidden Markov model and single sample image per person
2004 (English)In: 2004 IEEE international joint conference on neural networks, vols 1-4, proceedings, New York: IEEE conference proceedings, 2004, Vol. 1, 455-459 p.Conference paper (Refereed)Text
This paper presents a novel approach for recognizing faces in images taken from different illumination, expression, near frontal pose, partially occlusion and time delay. The method is based on one dimensional discrete Hidden Markov Model (1D-DHMM) with new way of extracting observations and using observation sequences. All subjects in the system share only one HMM that is used as a means to weigh a pair of observations. The Haar wavelet transform is applied to face images to reduce the dimension of the observation vectors. The selection of the recognized person is based on the highest score, which is the summation of the likelihoods of all observation sequences extracted from image on both vertical and horizontal dimensions. Our experiment results tested on the AR face database and the CMU PIE face database show that the proposed method outperforms the PCA, LDA, LFA based approaches tested on the same databases.
Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2004. Vol. 1, 455-459 p.
, IEEE International Joint Conference on Neural Networks (IJCNN), ISSN 1098-7576
IdentifiersURN: urn:nbn:se:umu:diva-122184DOI: 10.1109/IJCNN.2004.1379949ISI: 000224941900077ISBN: 0-7803-8359-1OAI: oai:DiVA.org:umu-122184DiVA: diva2:938566
IEEE International Joint Conference on Neural Networks (IJCNN), JUL 25-29, 2004, Budapest, HUNGARY