A Convolutional Neural Network for Real time Face Detection and Emotion Gender Classification
We implement a general Convolutional Neural Network (CNN) to design a real time model and validates our model by creating a real time vision that accomplishes the task of face detection, gender, and emotion classification simultaneously. We got accuracies of 95% in the IMDB WIKI age and gender dataset and 66% in the FER emotion recognition dataset. We have used a real time guided back propagation technique to visualize the weighed of real time CNN that uncovered the dynamic weight change and evaluate the learning feature. We think in the modern CNN architecture regularization and visualization of previously hidden layer features are necessary to reduce the gap between slow performances and real time architecture.