Customized Convolutional Neural Network for Detection of Emotions from Facial Expressions

Abstract

Abstract: The understanding and accurate interpretation of human emotions are crucial for effective interactions and communication. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have revolutionized emotion recognition by enabling the analysis of complex facial expressions. This paper focuses on a customized CNN architecture tailored for emotion detection from facial expressions. Utilizing datasets like CK+, FER2013, and JAFFE, preprocessing techniques are employed to enhance model stability and diversity. The proposed CNN architecture comprises convolutional and fully connected layers for feature extraction and classification, respectively. Experimental results, including accuracy, F1 score, and precision, demonstrate the effectiveness of the proposed CNN in accurately identifying various emotions compared to traditional machine learning methods. These findings underscore the potential of CNNs in advancing emotion recognition systems, promising significant applications across diverse fields, from marketing research to virtual reality.

Index Terms: Emotion Recognition, Facial Expressions, Convolutional Neural Networks, Customized Architecture, Image Processing.