DRCP: Dimensionality Reduced Chess Pattern for Person Independent Facial Expression Recognition

Abstract

Automatic Facial Expression Recognition (FER) has become essential today as it has many applications in real time such as animation, driver mood detection, lie detection, and clinical psychology. The effectiveness of FER systems mainly depends on the extracted features. For extracting distinctive features with low dimensions, a new local texture-based image descriptor named Dimensionality Reduced Chess Pattern (DRCP) is proposed for recognizing facial expressions in a person independent scenario. DRCP, an improvement over Chess Pattern (CP), is mainly proposed for effectively reducing the feature vector length of CP. For feature extraction, DRCP also considers the movements of chessmen in a [Formula: see text] neighborhood, as like CP. As a part of feature extraction through DRCP, apart from the center pixel, the remaining 24 pixels are arranged into four groups in such a manner that each group contains the pixels corresponding to three chessmen. From each group, one feature is extracted and thus corresponding to four groups, four features are extracted in a [Formula: see text] neighborhood. The extracted features are fed into multi-class Support Vector Machine (SVM) for expression recognition. The experiments are performed on five “in the lab” datasets (MUG, TFEID, JAFFE, CK[Formula: see text] and KDEF) and on two “in the wild” datasets (RAF and SFEW) in person independent setup to simulate a real world scenario.

DLSANet: Facial expression recognition with double‐code LBP‐layer spatial‐attention network

“…Kartheek, et al. [66] proposed a new local texture‐based image descriptor named dimensionality reduced chess pattern (DRCP) for FER in a person‐independent scenario. Niu, et al.…”

“…Liu, et al [65] proposed a dictionary recognition construction method based on collaborative low-rank and hierarchical-sparse representation to reduce the dependence of individuals on facial expression recognition and combined with label consist KSVD (LC-KSVD) to increase recognition accuracy. Kartheek, et al [66] proposed a new local texture-based image descriptor named dimensionality reduced chess pattern (DRCP) for FER in a person-independent scenario. Niu, et al [67] extracted more effective features from every image passed through a face detection algorithm.…”