Complex Facial Expression Analysis and Recognition using Deep Networks

Facial expression recognition plays a pivotal role in human-computer interaction. This work focuses on the development of a deep learning-based system for complex facial expression recognition. The methodology involves a comprehensive pipeline starting with the collection of input images utilizing OpenCV. The dataset comprises eight distinct expressions, each portrayed by two men and two women. Image acquisition leverages the 'haarcascade_frontalface_default.xml' for accurate facial detection during expression capture. Subsequently, a preprocessing stage refines the dataset for optimal model training. The core of the work lies in the implementation of a Convolutional Neural Network (CNN). The CNN architecture employs Rectified Linear Unit (ReLU) activation functions for feature extraction and utilizes softmax activation for classification purposes. Through iterative training, the model learns to discern nuanced facial expressions, enabling accurate recognition. This work aims to contribute to the advancement of deep learning-based facial expression recognition systems, potentially enhancing human-computer interaction across various domains.