کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
523651 868398 2009 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
چکیده انگلیسی

This paper presents a multimodal system for reliable human identity recognition under variant conditions. Our system fuses the recognition of face and speech with a general probabilistic framework. For face recognition, we propose a new spectral learning algorithm, which considers not only the discriminative relations among the training data but also the generative models for each class. Due to the tedious cost of face labeling in practice, our spectral face learning utilizes a semi-supervised strategy. That is, only a small number of labeled faces are used in our training step, and the labels are optimally propagated to other unlabeled training faces. Besides requiring much less labeled data, our algorithm also enables a natural way to explicitly train an outlier model that approximately represents unauthorized faces. To boost the robustness of our system for human recognition under various environments, our face recognition is further complemented by a speaker identification agent. Specifically, this agent models the statistical variations of fixed-phrase speech using speaker-dependent word hidden Markov models. Experiments on benchmark databases validate the effectiveness of our face recognition and speaker identification agents, and demonstrate that the recognition accuracy can be apparently improved by integrating these two independent biometric sources together.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Languages & Computing - Volume 20, Issue 3, June 2009, Pages 188–195
نویسندگان
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