Accurate, Fast & Robust Expression Recognition using Deep Learning
Previous state of the art approaches to facial expression recognition including our own relied on handcrafted feature extraction and computer vision pipelines optimized for runtime speed and accuracy on relatively small datasets. Using specialized deep learning architectures trained on much larger datasets, we have significantly improved accuracy over our previous academic and commercial efforts, even when both types of systems are trained on the same data. These efforts have led to marked improvements in robustness to head pose and expression variation, without incurring speed penalties, and without requiring the use of GPU acceleration.
Dr. Joshua Susskind is a senior data scientist at Emotient, a company focused on real-time perception of facial expressions from images and videos, where he develops algorithms and visualization techniques for understanding human behavior. In graduate school he developed the first deep nets that could recognize and generate facial expressions. He holds a PhD in Psychology and Machine Learning from the University of Toronto, where he was co-advised by Dr. Geoffrey Hinton and Dr. Adam Anderson. His academic work has been featured in high impact journals including Nature Neuroscience and Science and in top computer vision and machine learning conferences.