Analysis on techniques used to recognize and identifying the Human emotions

Abstract

Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwin’s work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress of research in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify the proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on the various recognition techniques used to identify the complexity in recognizing the facial expression is presented. This work will also help researchers and scholars to ease out the problem in choosing the techniques used in the identification of the facial expression domain.

Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing

“…The Viola and Jones face locator has become the deformity standard to assemble fruitful face recognition in ascertaining emotions progressively, in any case, it creates a high bogus positive (identifying a face when there is none) and bogus negative rate (not recognizing a face that is available) when straightforwardly applied to the information picture [8]. Different techniques and algorithms for emotional analysis considered from different researchers with accuracy analysed by authors [9].…”

Facial Emotion Recognition Using Asymmetric Pyramidal Networks With Gradient Centralization

“…Fusing HOG and CNN [67] 97.01% FaeveLiveNet based on Inception-RsNet [68] 98.0% Deep Covariance Trajectories [69] 98.4% Evolutional Spatial-Temporal Networks [70] 98.5% Hybrid feature descriptor [71] 98.5% Secondary Information aware Network [72] 99.5% [74] 91.81% Feature extraction based on gabor filter [75] 93.4% DNN based on residual blocks [76] 95.23% Proposed method 99.84%…”

Feature extraction comparison for facial expression recognition using adaptive extreme learning machine

“…Machine learning methods such as k-nearest neighbor (KNN) or support vector machine (SVM) have been widely used to solve this problem. However, previous study shows that neural network algorithm could achieved reliable accuracy even not the highest among hidden Markov model, AdaBoost, and SVM [9]. In other several studies found that the level of accuracy possessed by these algorithms is classified as lower when compared to the neural network (NN) model [10]- [12].…”