Detecting Smiles of Young Children via Deep Transfer Learning

Abstract

Smile detection is an interesting topic in computer vision and has received increasing attention in recent years. However, the challenge caused by age variations has not been sufficiently focused on before. In this paper, we first highlight the impact of the discrepancy between infants and adults in a quantitative way on a newly collected database. We then formulate this issue as an unsupervised domain adaptation problem and present the solution of deep transfer learning, which applies the state of the art transfer learning methods, namely Deep Adaptation Networks (DAN) and Joint Adaptation Network (JAN), to two baseline deep models, i.e. AlexNet and ResNet. Thanks to DAN and JAN, the knowledge learned by deep models from adults can be transferred to infants, where very limited labeled data are available for training. Cross-dataset experiments are conducted and the results evidently demonstrate the effectiveness of the proposed approach to smile detection across such an age gap.