Face Detection And Feature Extraction For Facial Emotion Detection


EOI: 10.11242/viva-tech.01.04.077

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Citation

Chetan Bhosale, Disha Jariwala, Tejas Keni, Karishma Raut, "Face Detection And Feature Extraction For Facial Emotion Detection", VIVA-IJRI Volume 1, Issue 4, Article 77, pp. 1-6, 2021. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Facial emotion Recognition has been a major issue and an advanced area of research in the field of Human- Machine Interaction and Image Processing. To get facial expression the system needs to meet a variety of human facial features such as color, body shape, reflection, posture, etc. To get a person's facial expression first it is necessary to get various facial features such as eye movement, nose, lips, etc. and then differentiate by comparing the trained data using differentiation appropriate for speech recognition. An AI-based approach to the novel visual system system is suggested. There are two main processes in the proposed system, namely Face detection and feature extraction.Face detection is performed using the Haar Cascade Method. The proper feature extraction method is used to extract the element and then used a vector machine to distinguish the final face shape. The FER13 data set is used for training.

Keywords

Emotion recognition, CNN, Machine learning, Python, AI.

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