Facial Emotion Recognition using Neighborhood Features

Abstract

We present a new method for human facial emotions recognition. For this purpose, initially, we detect faces in the images by using the famous cascade classifiers. Subsequently, we then extract a localized regional descriptor (LRD) which represents the features of a face based on regional appearance encoding. The LRD formulates and models various spatial regional patterns based on the relationships between local areas themselves instead of considering only raw and unprocessed intensity features of an image. To classify facial emotions into various classes of facial emotions, we train a multiclass support vector machine (M-SVM) classifier which recognizes these emotions during the testing stage. Our proposed method takes into account robust features and is independent of gender and facial skin color for emotion recognition. Moreover, our method is illumination and orientation invariant. We assessed our method on two benchmark datasets and compared it with four reference methods. Our proposed method outperformed them considering both the datasets.

An In-Depth Review of Deep Facial Expression Recognition: Obstacles, Utilizations, and Prospective Directions

“…Face detection is a foundational concept in computer vision that involves identifying and locating human faces within images or video frames [16]. This process relies on algorithms that analyze the visual patterns and unique attributes associated with human faces, such as the arrangement of eyes, nose, mouth, and other facial features.…”