High Efficiency Facial Expression Recognition based Active Semi Supervised Learning
Face expression recognition is an important area of computer vision research which has been extensively used for HCI (human computer interaction) identity confirmation, persons emotion recognition and different access control. It is difficult to recognize Human's facial expression in the real world due to various circumstances, such as face dissimilarity in pose, ages, lighting condition, occlusion and face motion. Large amount of labeled and trained facial expression data can be one of the key idea. If the training dataset and test dataset collect from a particular person and environment the chances of getting higher accuracy is possible due to similar variation in facial expression. In this paper, we propose a method for gathering many facial expression data within an indoor environment and train them through ASSL(Active Semi Supervised Learning) framework to gain high accuracy. We gather a large number of unlabeled facial expression data from intelligent technology Lab members of Inha University and BU3DFE (Binghamton University 3D Facial Expression benchmark datasets. We train our initial model with the ASSL framework using deep learning network VGG(visual geometry group, University of Oxford) model. Our framework adopted MTCNN(Multi task Cascaded Convolutional Networks) detector for face detection and we also modify the last two layer of VGG network for better performance. Repeat this entire process support us to get better performance improvement. Therefore using the ASSL method, we gain better performance and higher accuracy with less labor force. Our experimental result shows the high efficiency with various training data.