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.

翻译：特征选择可定义为优化问题，并通过仿生算法求解。蜜蜂算法在特征选择优化任务中展现出良好性能。另一方面，局部相位量化是一种频域特征，在深度图像上表现优异。本研究从伊朗Kinect面部数据库的彩色和深度图像中提取LPQ特征后，应用蜜蜂特征选择算法选取所需特征数量以完成最终分类任务。IKFDB使用Kinect传感器V.2记录，包含用于面部及面部微表情识别的彩色与深度图像。本研究采用愤怒、快乐、惊讶、厌恶和恐惧五种面部表情进行最终验证。将所提出的蜜蜂LPQ方法与粒子群优化LPQ、主成分分析LPQ、套索LPQ及纯LPQ特征进行比较，分类任务采用支持向量机、K近邻、浅层神经网络和集成子空间KNN。结果表明，相比其他方法，所提算法表现出色（准确率达99%）。