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

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