Facial expression recognition feature based approaches to deep learning techniques

Abstract

Facial expression recognition (FER) is a problem of pattern recognition that invites the attention of computer vision researchers for the last three decades. However, the problem is still alive due to challenges such as blurring, illumination variation, pose variation, face image captured in the unconstrained environment, and so on. In the beginning, hand-crafted features followed by classical classification mechanism through a classifier have been studied for various features as well as various classifiers. The hand-crafted features that are associated with changes in expression are hard to extract due to the individual distinction and variations in emotional states. With the induction of deep neural network (DNN) and convolution neural network (CNN), a change in the techniques of facial expression recognition is observed both in terms of efficiency and handling various challenges mentioned above. The modular approach presented here mimics the capability of the human to identify a person with a limited facial part. Facial parts like eyes, nose, lips, and forehead contribute more to the expression recognition task. In this thesis, we have addressed classical feature-based approaches to deep learning techniques. This thesis presents approaches for Facial Expression Recognition (FER). Firstly, we propose two dimensional Taylor expansion for the facial feature extraction as well as to handle the local illumination. Most procedures just used the arrangement with global illumination varieties and thus yielded more unsatisfactory recognition performances within the case of natural illumination variations that are usually uncontrolled within the globe. Hence, to address the brightening variety issue, at that point we presented the (LL) Laplace-Logarithmic area in this article for further improving the exhibition. We applied the proposed 2D Taylor expansion theorem in the facial feature extraction phase and formulated the 2DTFP method.