Human Abnormality Classification Using Combined CNN-RNN Approach

With the advent of big data, Facial Expression Recognition (FER) has become a promising area in the Deep Learning domain. The facial expression reflects our mental activities and provides useful information on human behaviors. With the increasing improvement of the deep learning-based classification method, special demands for human stability measurement using facial expression have emerged. Recognizing human abnormalities such as drug addiction, autism, criminal mentality, etc., are quite challenging due to the limitation of existing FER systems. Besides, there are no existing datasets that consist of helpful images that describe the true expressions of the human face that can detect human abnormality. To achieve the best performance on human abnormality recognition we have created a Normal and Abnormal Humans Facial Expression (NAHFE) dataset. In this paper, we propose a new model by stacking the Convolutional Neural Network and Recurrent Neural Network (RNN) together. The proposed combined method consists of convolution layers followed by the recurrent network. The associated model extracts the features within facial portions of the images and the recurrent network considers the temporal dependencies which exist in the images. The proposed combined architecture has been evaluated based on the mentioned NAHFE dataset and it has achieved state-of-the-art performance to detect human abnormalities.