Evaluation of Smile Detection Methods with Images in Real-World Scenarios

Discriminative methods such as SVM, have been validated extremely efficient in pattern recognition issues. We present a systematic study on smile detection with different SVM classifiers. We experimented with linear SVM classifier, RBF kernel SVM classifier and a recently-proposed local linear SVM (LL-SVM) classifier. In this paper, we focus on smile detection in face images captured in real-world scenarios, such as those in GENKI4K database. In the meantime, illumination normalization, alignment and feature representation methods are also taken into consideration. Compared with the commonly used pixel-based representation, we find that local-feature-based methods achieve not only higher detection performance but also better robustness against misalignment. Almost all the illumination normalization methods have no effect on the detection accuracy. Among all the SVM classifiers, the novel LL-SVM is verified to find a balance between accuracy and efficiency. And among all the features including pixel value intensity, Gabor, LBP and HOG features, we find that HOG features are the most appropriate features to detect smiling faces, which, combined with RBF kernel SVM, achieve an accuracy of \(93.25\,\%\) on GENKI4K database.