Abstract—Facial expression recognition is one of the most challenging research areas in the image recognition field and has been studied actively for a long time. But it has not achieved enough performance under the practical environment yet. Especially, smile is the most important facial expression used to communicate well between human beings and also between human and machines. Therefore, if we can detect smile and also estimate it's intensity at low calculation cost and high accuracy, it will raise the possibility of inviting many new applications in the future. In this paper, we focus on smile in facial expressions and study feature extraction methods to detect a smile and estimate its intensity only by facial appearance information (Facial parts detection, not required). We use Local Intensity Histogram (LIH), Center-Symmetric Local Binary Pattern (CS-LBP) or features concatenated LIH and CS-LBP to train Support Vector Machine (SVM) for smile detection. Moreover, we construct SVM smile detector as a cascaded structure both to keep the performance and reduce the calculation cost, and estimate the smile intensity by posterior probability. As a consequence, we confirmed that our proposed method provided the comparable performance with the existing method, and it also achieved both low calculation cost and high performance even with the practical database.
Index Terms—Face, facial expression, smile intensity, SVM.
The authors are with the Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibraki 305-8568, Japan (e-mail: shimada.kb@ aist.go.jp, email@example.com, firstname.lastname@example.org).
Cite: Keiji Shimada, Yoshihiro Noguchi, and Takio Kuria, "Fast and Robust Smile Intensity Estimation by Cascaded Support Vector Machines," International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 24-30, 2013.