Real-time Convolutional Neural Networks for Emotion and Gender Classification

Understand

In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs.

• We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture.
• After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets.
• We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset.