International Conference on Computer and Knowledge Engineering

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Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis

Authors :

Phee Wei Qin¹ Ervin Gubin Moung² Ali Farzamnia³ Farashazillah Yahya⁴ John Julius Danker Khoo⁵ Maisarah Mohd Sufian⁶

1- Faculty Of Computing And Informatics Universiti Malaysia Sabah (UMS) 2- Faculty Of Computing And Informatics Universiti Malaysia Sabah (UMS) 3- Faculty of Engineering, Universiti Malaysia Sabah 4- Faculty Of Computing And Informatics Universiti Malaysia Sabah (UMS) 5- Faculty of Computing and Informatics Univerisity Malaysia Sabah 6- Faculty of Engineering, Universiti Malaysia Sabah

Keywords :

Emotion،Machine Learning،CNN،facial expression recognition،color space،sentiment analysis

Abstract :

Automated facial expression recognition is popular for diagnosing mental diseases and interpreting human emotions. Image sentiment analysis helps incorporate feedback for improved product development. However, there's a lack of research on the effect of different color spaces on facial expression recognition performance. Thus, there are three objectives in this project, which are to develop a deep learning-based facial expression recognition system for image sentiment analysis. Second, to investigate the effect of different color spaces on facial expression recognition and select the best one. Lastly, to evaluate the accuracy of the system on the AffectNet dataset. The color spaces to be tested are RGB, YCBCR, HSV, XYZ, and YIQ. The methodology involves selecting the AffectNet dataset, pre-processing the images, building a Convolutional Neural Network model, and evaluating performance using metrics like accuracy, precision, recall, and F1-score.