Feature selection using improved multiobjective and opposition-based competitive binary gray wolf optimizer for facial expression recognition
Author:
Affiliation:
1. Prestige Institute of Management and Research, Gwalior, Madhya Pradesh
2. MITS, Department of Computer Science and Engineering, Gwalior, Madhya Pradesh
3. Jiwaji University, SOS Computer Science and Application, Gwalior, Madhya Pradesh
Publisher
SPIE-Intl Soc Optical Eng
Subject
Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics
Reference38 articles.
1. Communication without words;Mehrabian,2017
2. Automated Facial Expression Recognition Using Gradient-Based Ternary Texture Patterns
3. Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order
4. Facial expression recognition based on transfer learning from deep convolutional networks
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1. A New Facial Expression Recognition Algorithm Based on DWT Feature Extraction and Selection;The International Arab Journal of Information Technology;2024
2. Three-phases hybrid feature selection for facial expression recognition;The Journal of Supercomputing;2023-11-13
3. Analytical analysis on the static support reactions of single-column pier bridges using the grey wolf optimizer;Structures;2023-09
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