Abstract:

Facial Expression Recognition (FER) has become a promising area in the Deep Learning domain with the advent of big data. The facial expression reflects our mental activities and provides valuable information on human behaviours. With the increasing improvement of the deep learning based classification method, particular 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 human face’s genuine expressions that can detect human abnormalities. To achieve the best performance on human abnormality recognition, we have created a Normal and Abnormal Humans Facial Expression (NAHFE) dataset. This thesis paper proposes a new model by stacking the Convolutional Neural Network. 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.