The deployment of Facial Expression Recognition (FER) systems in resource-constrained edge computing environments is hindered by the high computational demands that often impede their operational efficacy. This paper introduces the Lightweight Attentional Convolutional Capsule Network (LAC-Capsnet), which has been carefully designed to achieve competitive classification performance while requiring a significantly reduced cardinality of trainable parameters. Its inherent novelty is the synergistic combination of an attentional mechanism (STN for geometric invariance and SE block for channel recalibration) with effective microarchitectures (Inception, Depthwise Separable Convolutions). To leverage dynamic routing and achieve a more refined spatial hierarchy modeling, this convolutional backbone is seamlessly integrated with a capsule network. Based on comprehensive validation, LAC-Capsnet achieves classification accuracy of 97.15% on CK + and 66.70% on FER2013, while requiring only 0.97 million parameters. As a result, it was demonstrated to be a cost-effective and lightweight FER solution that showed efficacy and computational parsimony.

Local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic‐based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP.