Abstract
A smile is a specific movement of face muscles to relay an optimistic feeling. A smile represents satisfaction and happiness. Many application created using smile detection technology, for example product rating, patient monitoring, image capturing, video conferencing and interactive systems. There are many smile detections techniques have been proposed for smile detection in the unconstrained scenarios. However, the dimensions of most notable feature descriptors are humongous, which is challenging in real-time applications. Besides, feature should be more powerful to identify between smiling and non-smiling face. The proposed method has two consecutive actions: 1) amalgamation of geometric feature extraction (GFE) and regional local binary pattern (LBP) features extraction using autoencoders; 2) Kohonen selforganizing map (KSOM) is adopted to classify smile based on these features. The proposed method is mathematics more dynamic and performance wise more precise. The performance of the propounded approach is proved on GENKI-4K database.
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