Facial Expression Recognition from Different Angles Using DCNN for Children with ASD to Identify Emotions

In this paper, continued work of a research project is discussed whose end goal is to build a mobile device application that can teach children with ASD (Autism Spectrum Disorder) to recognize human facial expressions utilizing computer vision and image processing. This paper discusses the intermediate work of a facial expression recognition approach using a deep convolutional neural network (DCNN) utilizing images from different angles. The Karolinska Directed Emotional Faces (KDEF) dataset has been used to train and test with the DCNN model. This dataset contains images from five different angles. Results of this paper will contribute to the end goal of the research which is to recognize facial expression from any angle of viewpoint. Finally, the result obtained is discussed and future work of the project is outlined.