Smile detection in unconstrained scenarios using self-similarity of gradients features

Smile detection in unconstrained scenarios is a hot research topic with many real-world applications. This paper presents a new approach to practical smile detection and the primary contributions are three-fold. (1) In the image registration procedure, an eyes-mouth alignment strategy is found to be more efficient than popular eyes alignment. (2) In the feature extraction procedure, a novel feature descriptor, Self-Similarity of Gradients (GSS), is proposed and achieved good performance in comparison with baseline approaches. (3) Feature combination and multi-classifier combination strategies are adopted in experiments and excellent results are obtained. Experimental results show that the combined features (HOG+GSS) using AdaBoost+SVM achieve improved performance over state-of-the-art in the GENKI4K benchmark.