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

arXiv: https://arxiv.org/abs/2308.01700 · HTML: https://mlsanity.com/paper/2308.01700/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Nature-Inspired Algorithms for Wireless Sensor Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.10453/) (2020)

## Built on by (most cited first, 3 of 3)

- [Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data](https://mlsanity.com/paper/2501.16353/) (2025)
- [Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition](https://mlsanity.com/paper/2503.14513/) (2025)
- [MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset](https://mlsanity.com/paper/2509.05330/) (2025)

## Abstract (arXiv)

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.