Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions Recognition

TL;DR

This paper introduces a novel feature selection method combining Bees Algorithm with Local Phase Quantization features for RGB-D facial expression recognition, achieving high accuracy on the IKFDB dataset.

Contribution

The study proposes a new Bees Algorithm-based feature selection approach for LPQ features in RGB-D facial expression recognition, outperforming existing methods.

Findings

Achieved 99% classification accuracy.

Outperformed PSO, PCA, Lasso, and baseline LPQ methods.

Effective in selecting relevant features for facial expression recognition.

Abstract

Feature selection could be defined as an optimization problem and solved by bio-inspired algorithms. Bees Algorithm (BA) shows decent performance in feature selection optimization tasks. On the other hand, Local Phase Quantization (LPQ) is a frequency domain feature which has excellent performance on Depth images. Here, after extracting LPQ features out of RGB (colour) and Depth images from the Iranian Kinect Face Database (IKFDB), the Bees feature selection algorithm applies to select the desired number of features for final classification tasks. IKFDB is recorded with Kinect sensor V.2 and contains colour and depth images for facial and facial micro-expressions recognition purposes. Here five facial expressions of Anger, Joy, Surprise, Disgust and Fear are used for final validation. The proposed Bees LPQ method is compared with Particle Swarm Optimization (PSO) LPQ, PCA LPQ, Lasso LPQ, and just LPQ features for classification tasks with Support Vector Machines (SVM), K-Nearest Neighbourhood (KNN), Shallow Neural Network and Ensemble Subspace KNN. Returned results, show a decent performance of the proposed algorithm (99 % accuracy) in comparison with others.