Published Paper Details:

This paper presents a Facial Emotion Detection System with Expression Recognition, utilizing Convolutional Neural Networks (CNNs) implemented with TensorFlow and Keras. The system operates in two phases: detecting faces in images and then recognizing their expressions. The combination of deep learning and emotion recognition has significant potential across various fields. This work focuses on "Image-based emotion detection using deep learning," leveraging CNNs to identify and classify facial expressions, which are key to understanding human emotions. CNNs are used for their ability to extract detailed features from images. Additionally, the wavelet transform is integrated to enhance the accuracy of recognizing subtle emotional nuances. The study utilizes a dataset from Kaggle, containing seven distinct facial expressions, for experimental analysis. Results indicate that the CNN method, improved with wavelet transform, significantly boosts accuracy in facial expression recognition. This innovative approach promises applications in human-computer interaction and mental health assessment, providing a pathway for precise and nuanced emotional interpretation of facial images.