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http://dx.doi.org/10.13067/JKIECS.2020.15.1.85

Optimization of Deep Learning Model Based on Genetic Algorithm for Facial Expression Recognition  

Park, Jang-Sik (Dept. Electronic Engineering, Kyungsung University)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.15, no.1, 2020 , pp. 85-92 More about this Journal
Abstract
Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.
Keywords
Activation function; Convolution kernel; Genetic algorithm; Training dataset; Optimizer algorithm;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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