Region-Based Facial Expression Recognition in Still Images

Abstract

Abstract-In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

Real-Time Facial Expression Recognition App Development on Smart Phones

“…Classification Methods: SVM classifier [33], [34] is the most common classifier that has been applied to facial expression recognition [40], [33], [34], [41], [42]. Several works have reported that SVM with linear kernel produce similar test outcomes when compared to radial basis function kernel (RBF) [33], [43], [34].…”

Event-Related Potentials Related to Anxiety in Emotion-Attention Interaction

“…According to earlier work related to anxiety, patients with anxiety disorder show greater emotional confusion, attentional bias, and cognitive damage compared to people without such disorders [7][8][9][10][11]. These studies investigated ERP characteristics using IAPS images, emotional faces, and emotional words as the emotional stimuli presented.…”

Facial Expression Recognition and Analysis: A Comparison Study of Feature Descriptors

“…Haar descriptor [2] is also popular for FER [25], [26]. Recently, LBP descriptor [3] has been adopted for FER [11], [15], [16], [18], [27] and HOG descriptor [4] has been examined for FER [28], [29], [30]. In this study, we also evaluate the performance a relatively new feature descriptor called BRIEF descriptor [5] in FER.…”

“…Dense SIFT descriptor [31] has also been used for FER recently [32]. Classification Methods: SVM classifier [33], [34] is the most common classifier that has been applied to FER [11], [20], [21], [22], [23], [25], [27], [28], [29], [30]. Several works have reported that SVM with linear kernel produce similar test outcomes when compared to radial basis function kernel (RBF) [20], [21], [22], [23].…”