A Convolutional Neural Network (CNN) model for facial expression recognition is specifically designed to analyze facial images and predict the emotions based on the expressions displayed.
To train the CNN model, a large dataset of labeled facial images with corresponding emotion labels is required. The model is trained using an optimization algorithm that adjusts the internal parameters of the network to minimize the difference between predicted and true emotion labels. This process involves feeding the training data through the network, computing the loss, and updating the model's parameters using techniques like backpropagation and gradient descent.
Once trained, the CNN model can be used to recognize facial expressions and predict the emotions of new, unseen faces. It provides a valuable tool for understanding and analyzing human emotions, with applications in areas such as psychology, human-computer interaction, social robotics, and market research.
To accurately face recognition using Computer Vision + Deep Learning technique. To accurately emotion recognition based on facial expressions using Computer Vision + Deep Learning technique.