A Real-time Facial Expression Recognizer using Deep Neural Network

Jinwoo Jeon, Jun-Cheol Park, Youngjoo Jo, Changmo Nam, Kyung-Hoon Bae, Youngkyoo Hwang, Dae-Shik Kim

Abstract: With increasing boundaries of deep learning based recognition model, particular demands for real-time user state recognizer targeting smart home devices have emerged. This paper suggests real-time facial expression recognizer to satisfy them. We use HOG feature descriptor to detect a human face, correlation tracker to track detected face and deep Convolutional Neural Network (CNN) based recognizer on our model. Our CNN model is trained and tested with Kaggle facial expression recognition challenge dataset. The experimental result shows that high test accuracy and low computation time are achieved by our recognizer enabling real-time high-performance human expression recognition for mobile use.