A Survey of Facial Expression Recognition Methods

Abstract

-Facial expression recognition (anger, sad, happy, disgust, surprise, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression is one of the most powerful and natural means for human beings to show their emotions. It has found its applications in humancomputer interaction (HCI), robotics, border security systems, forensics, machine vision, video conferencing, user profiling for customer satisfaction, physiological research etc. Although humans can detect facial expressions with less effort and delay but it is still a challenge for the machine to fast and effectively detect facial expressions. Therefore algorithms should be developed to thought machines to understand facial gestures. This paper focuses on a review of different techniques for facial expression recognition.

Intelligent facial emotion recognition using a layered encoding cascade optimization model

“…According to Swets and Weng [17], PCA derives the most expressive features, but may not embed sufficient discriminating power. In addition to PCA, Fisher Linear Discriminant (FLD) is another commonly used feature reduction technique which is claimed to provide comparatively more class separability by maximizing the mean between classes and minimizing the variation within a class [3,18,19]. Thus FLD projects the most discriminative features for class distinction.…”

Facial expression recognition in the wild using rich deep features

“…A typical static facial expression recognition approach could be divided into four main components [3] face detection, face alignment, feature extraction and finally classification. As for the step of feature extraction of expression, most of the recent approaches presented in the literature design a handcrafted feature extraction method or uses a combination of many features [4]. In contrast, this paper is based on rich deep features extracted using Deep Convolutional Neural Networks 1 (CNNs) [5].…”