Autors: Dinkova P., Georgieva P., Manolova, A. H., Milanova M.
Title: Face recognition based on subject dependent Hidden Markov Models
Keywords: Hidden Markov models , Face recognition , Face , Training , Image recognition , Quantization (signal) , Feature extraction

Abstract: In this paper we present an automatic face recognition system based on incremental Singular Values Decomposition (SVD) and subject dependent Hidden Markov Models (HMM). For each subject, an individual HMM is trained with features, extracted from the orthogonal decomposition (SVD) of the subject's training images. The main advantage of the proposed SVD-HMM recognition system is the robustness against image dimensionality reduction. The system was tested on two benchmark face datasets - the Olivetti Research Laboratory (ORL) and the YALE database. The SVD-HMM was further compared with a standard SVD face recognition. SVD applied to the original (full size) images performs similarly to the SVD-HMM applied to the compressed (half of the original size) images. SVD degrades rapidly when the image is compressed.

References

    Issue

    IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp. 1-5, 2016, Bulgaria, IEEE, DOI 10.1109/BlackSeaCom.2016.7901570

    Copyright IEEE

    Цитирания (Citation/s):
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    Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science