In this paper, we propose a low complexity smile detection technique, able to detect smiles in a variety of light conditions, face positions, image resolutions. The proposed approach firstly detects the faces in the image, then applies almost cost-free mouth detection, extracts features from this region and finally classifies between smiling and nonsmiling stages. In this paper different feature extraction methods and classification techniques are analyzed from both the performance and computational complexity standpoints. The best compromise between performances and complexity is represented by a combined approach which exploits both a shape feature and a texture feature and uses the Mahalanobis distance based classifier. This solution achieves good performances with very low complexity, being suitable for an implementation on mobile devices.

Date Published: 6 March 2013
PDF: 10 pages
Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610O (6 March 2013); doi: 10.1117/12.2002449

Published in SPIE Proceedings Vol. 8661:
Image Processing: Machine Vision Applications VI
Philip R. Bingham; Edmund Y. Lam, Editor(s)