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Volume: 29 | Article ID: art00013
A Real-time Smile Elegance Detection System: A Feature-level Fusion and SVM Based Approach
  DOI :  10.2352/ISSN.2470-1173.2017.10.IMAWM-173  Published OnlineJanuary 2017

Smile detection in the unconstrained real-world scenario has attracted much attention due to its importance for mobile applications and human computer interaction. However, in many applications, how to determine the attractiveness of a smile face image (i.e., smile elegance) is an interesting task. In this paper, we present a real-time smile elegance detection system based on feature-level fusion and support vector machine (SVM). Specifically, three types of features, including local intensity histogram (LIH), central symmetry local binary pattern (CS-LBP) and Gabor wavelet, are firstly employed to characterize the global and local relationships of the face image. Then, a feature-level fusion method is leveraged to effectively combine these features. Finally, a specific SVM classifier is learnt to classify the smile face image into elegance or inelegance. Since there is no available public smile elegance database, we establish one for the first time. Experimental results on our collected smile elegance database demonstrate the effectiveness and efficiency of the proposed smile elegance detection system.

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Lili Lin, Yiwen Zhang, Weini Zhang, Zhihui Chen, Yan Yan, Tianli Yu, "A Real-time Smile Elegance Detection System: A Feature-level Fusion and SVM Based Approachin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2017,  pp 80 - 85,

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Electronic Imaging
Society for Imaging Science and Technology