Automated facial expression recognition based on histograms of oriented gradient feature vector differences

Abstract

This article proposes an efficient automated method for facial expression recognition based on the histogram of oriented gradient (HOG) descriptor. This subjectindependent method was designed for recognizing six prototyping emotions. It recognizes emotions by calculating differences on a level of feature descriptors between a neutral expression and a peak expression of an observed person. The parameters for the HOG descriptor were determined by using a genetic algorithm. Support vector machines (SVM) were applied during the recognition phase, whereat one SVM classifier was trained for one emotion. Each classifier was trained using difference vectors obtained by subtraction of HOG feature vectors calculated for the neutral and apex emotion subjects image. The proposed method was tested by using a leave-one-subject-out validation strategy for 106 subjects on 1232 images from the Cohn Kanade, and for 10 subjects on 192 images from the JAFFE database. A mean recognition rate of 95.64 % was obtained using the Cohn Kanade database, which is higher than the recognition rates for almost all other single-image-or video-based methods for facial emotion recognition.

A hybrid feature descriptor with Jaya optimised least squares SVM for facial expression recognition

“…It is evident from the table that the proposed system leads to achieving the highest classification accuracy compared to all others over both datasets such as CK+ and JAFFE. It can also be observed that Siddiqi et al [15] and Mlakar and Potocinik [6] achieved comparable results on CK+ dataset. Further, a better performance was attained on JAFFE dataset by Zhang and Tjondronegoro [7], Hu et al [30], and Siddiqi et al [15].…”

“…The grid search technique has been served as the conventional procedure for obtaining the best results despite it possessing limitations like high computational burden and getting stuck at local minima. Recently, it has been observed that the optimisation methods such as moth‐flame optimisation (MFO) algorithm [43] and genetic algorithm (GA) [6] are widely used to obtain the global best solution as compared to the grid‐search method. Therefore, in this study, the simplest and yet effective optimisation technique, Jaya, is introduced to identify the parameters of RBF based LS‐SVM, and the resultant model is referred to as JOLS‐SVM that is used to predict the emotion class.…”

“…Happy and Routray The hidden conditional random fields (HCRFs) are harnessed for classification. In [6], a subject-independent method was proposed by calculating the difference between the peak and neutral expression of a person. The optimal parameters of the HOG feature descriptor are computed using genetic algorithm (GA).…”

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

“…e results indicate that the fear expression misclassified either as anger or disgust emotion. e reason is that the fear, disgust, and anger expressions demonstrated similar muscle activities [37]. Moreover, it is also observed that the average recognition accuracy rate of the CK+ dataset is slightly higher than the MMI dataset.…”

Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

“…Some of the earlier techniques that utilised facial dynamics to analyse expressions used dynamic Bayesian networks [20] and hidden Markov models [21]. For the given input neutral and emotional images, Mlakar and Potocnik [22] computed histogram of oriented gradient based feature descriptors characterizing the differences between the two input frames. Wehrle et al [23] observed in their experiments that in a video clip advancing from neutral to emotional, the reference state of the subject was known and concluded that dynamic methods employing temporal information led to a better perception of facial expressions.…”