An exposition of facial expression recognition techniques

Automated facial expression recognition schemes have been a subject of interest ever since the inception of its idea. The initial efforts required supervised and controlled environments to get realistic and convincing experimental results. Various improved methods have been suggested to detect the facial expressions of a human. Some of the popularly used methods are automatic expression recognition system (AERS), graph-preserving sparse nonnegative matrix factorization (GSNMF) algorithm, two-phase test sample representation (TPTSR) technique and temporal template method. The current state-of-the-art techniques are able to detect facial expressions in difficult and obscured environments. Advancements in this technique have helped researchers to use video sequences as images to detect expressions. A single image is extracted from a video sequence, and elaborate techniques are applied to detect the expression. This article endeavors to discuss these intricate techniques and critically analyzes them. It helps the reader to understand the contemporary problems in facial recognition systems and how various researchers have employed different models to overcome these challenges. In this paper, the performance of various techniques such as AERS, GSNMF algorithm, TPTSR, performance-based character animation, temporal template method, feature extractions using Gabor filter and image sequencing-based methods has been scrutinized in terms of their efficiency, accuracy and effectiveness. The efficiency and accuracy of the techniques have been compared using various benchmarks such as leave one out, cross-validation and receiver operating characteristics. Each technique bears its own advantages and disadvantages in terms of accuracy and efficiency. The highest accuracy rate is exhibited by the technique using canny edge detection algorithm and chamfer image method.