Face Detection and Emotion Recognition

Face detection is a core task in computer vision that focuses on recognizing and pinpointing human faces in images or video frames. This paper introduces a comprehensive machine learning (ML) model tailored for the dual tasks of face detection and emotion recognition. For the initial phase of face detection, the Haar cascading model is employed to accurately identify facial features. This model focuses on detecting the edges of the face, utilizing key components such as the eyes and lips to delineate these contours. Once the face is successfully isolated from the input image, the subsequent stage involves leveraging deep learning concepts and Convolutional Neural Network (CNN) algorithms for emotion recognition. The implementation of these advanced techniques is slated for later stages of the project. Emotion recognition will be performed on the extracted facial image, enabling the model to discern and classify emotions with enhanced precision. This dual-stage approach ensures a robust and specialized framework for both face detection and emotion recognition, combining the efficiency of traditional Haar cascading with the power of deep learning algorithms.