Facial expression recognition using local Gabor filters and PCA plus LDA

A simple smile can indicate our approval, happiness or positive thoughts, while a scowl might signal displeasure or anger. Understanding facial expressions and their meanings are crucial not only in our daily life communication, but also in many applications. For example, in marketing, the customers' facial expressions indicate their response towards a product. In artificial intelligence (AI), robots can use human facial expressions as a cue for understanding their emotion in order to respond appropriately. This paper proposes a method for recognizing human facial expressions from images using local Gabor filter, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The system starts by applying the face detection algorithm to detect the face from an image. From the face, the system extracts the Gabor filter responses and maps these responses into the novel feature subspace using the joined framework of PCA and LDA. Note that, in our framework, the principle component removal is also integrated into the framework. Based on the weighted neighbor approach, the system finally classifies human expressions into 4 different classes: anger, surprise, happiness and neutral. The results demonstrate that our approach significantly outperforms the baselines.