Journal Title : International Journal of Modern Trends in Engineering and Science
Volume 03 Issue 05 2016
ISSN no: 2348-3121
Page no: 16-18
Abstract- This paper proposes an approach called Extreme Sparse Learning (ESL), which has the ability to jointly learn a dictionary (set of basis) and a non-linear classification model. The proposed approach combines the discriminative power of Extreme Learning Machine (ELM) with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. Additionally, this work presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.
Keywords—Extreme Sparse Learning, non-linear, spatio-temporal
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