Extraction of informative regions of a face for facial expression recognition

Abstract

The aim of facial expression recognition (FER) algorithms is to extract discriminative features of a face. However, discriminative features for FER can only be obtained from the informative regions of a face. Also, each of the facial subregions have different impacts on different facial expressions. Local binary pattern (LBP) based FER techniques extract texture features from all the regions of a face, and subsequently the features are stacked sequentially. This process generates the correlated features among different expressions, and hence affects the accuracy. This research moves toward addressing these issues. The authors' approach entails extracting discriminative features from the informative regions of a face. In this view, they propose an informative region extraction model, which models the importance of facial regions based on the projection of the expressive face images onto the neural face images. However, in practical scenarios, neutral images may not be available, and therefore the authors propose to estimate a common reference image using Procrustes analysis. Subsequently, weighted‐projection‐based LBP feature is derived from the informative regions of the face and their associated weights. This feature extraction method reduces miss‐classification among different classes of expressions. Experimental results on standard datasets show the efficacy of the proposed method.

Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition

“…There are well-known binary features such as local binary pattern (LBP) [60], Binary Robust Invariant Scalable Keypoints (BRISK) [61], Binary Robust Independent Elementary Features (BRIEF) [62], and the Oriented FAST and Rotated BRIEF (ORB) [63]. Among them, the LBP is widely utilized in computer vision, because it is a computationally simple non-parametric texture descriptor and can handle monotonic illumination variations well in the textured images [29,60,64]. Therefore, we used Local Binary Pattern (LBP) feature [60].…”

Facial Emotion Recognition Using NLPCA and SVM

“…Kumar et al [14] proposed a framework depending on getting the instructive regions of a face image. In the feature extraction stage, LBP features were extracted and then Procrustes analysis was used to model the reference image.…”

“…Input space does not separate training set linearly, but in the case of the attribute space, the training set will be linearly separable. This is known as the "Kernel trick" Proposed by Shenetal [14][15].…”

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

“…The obtained features are then classified using Sparse representation classifier. S. Kumar et al, [27] have used Local Binary Pattern descriptor to extract discriminative features from informative regions of face and the extracted informative region will estimates the importance of sub regions using projection analysis of expressive images. It is found that the performance of the proposed method is better than the existing methods on JAFFE face database given in Table 7.…”