Smile detection in the wild with hierarchical visual feature
Smile detection in the wild is an interesting and challenging problem. This paper presents an efficient approach with hierarchical visual feature to handle this problem. In our approach, Gabor filters with multi-scale, multi-orientation are first applied to extract facial textures namely Gabor faces from the input face image. After this, Histograms of Oriented Gradients (HOG) are employed to encode these extracted Gabor faces to capture and characterize the facial appearance characteristics. We further adopt a pooling strategy to transform the multiple HOG features into a global visual feature called Gabor-Hog. Finally, SVM is trained to perform the classification. The experiments conducted on the GENKI4K database show that the proposed visual feature is robust to distinguish a smile face from a no-smile face. Our method also achieves a promising performance compared with the other state-of-the-art methods.