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

Abstract. 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 experiment-ed 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 normal-ization, alignment and feature representation methods are also taken into consideration. Compared with the commonly used pixel-based represen-tation, 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.

Smile detection in the wild based on transfer learning

Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which lead to multiple applications, e.g., human behavior analysis and interactive controlling. Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. To leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection, an efficient transfer learning-based smile detection approach is proposed in this paper. Unlike previous works which use either hand-engineered features or train deep convolutional networks from scratch, a well-trained deep face recognition model is explored and fine-tuned for smile detection in the wild. Three different models are built as a result of fine-tuning the face recognition model with different inputs, including aligned, unaligned and grayscale images generated from the GENKI-4K dataset. Experiments show that the proposed approach achieves improved state-of-the-art performance. Robustness of the model to noise and blur artifacts is also evaluated in this paper