I. Masi, W. AbdAlmageed, F. Chan, J. Choi, S. Harel, J. Kim, K. Kim, J. Leksut, S. Rawls, Y. Wu, T. Hassner, G. Medioni, L.-P. Morency, P. Natarajan, R. Nevatia. Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Facial expression is a rich source of information which provides an important communication channel for human interaction. People use them to reveal intent, display affection, and express emotion. Automated tracking and analysis of such visual cues would greatly benefit human-computer interaction. A crucial initial step in many affect sensing, face recognition, and human behavior understanding systems is the detection of certain facial feature points such as eyebrows, corners of eyes, and lips.
While facial landmark detection algorithms have seen considerable progress over the recent years, they still struggle under occlusion, in adverse lighting conditions and the presence of extreme pose variations. Our work specifically focuses on addressing such challenging scenarios using various computer vision and machine learning techniques.