Title: Deep Learning Approaches Face Analysis

Biography:

Will update shortly.

Abstract

Deep learning has shown impressive performances in many problems of artificial intelligence including face analysis which has been a challenging task in computer vision for decades. Face analysis has various application areas such as video surveillance, human-computer interaction and driver fatigue detection. In this talk, I will give a brief overview of deep learning approaches in face analysis. After detecting the face regions in an input image the first step is to pre-process those regions for the subsequent stages. Although the pre-processing operations differ based on the application of analysis I will be focusing on the deep learning methods developed for face alignment, pose estimation, face formalization and face super-resolution problems. Other subjects in the talk are related to face attribute estimation, face expression and emotion analysis and face recognition. Face attributes include age, gender and other attributes such as glass/no glass and beard/no beard. In facial expression/emotion analysis I will discuss the methods in recognition of prototypical facial expressions, i.e. anger, disgust, fear, happiness, sadness and surprise in addition to facial action units. The face recognition part of the talk covers both the identification and verification problems. The talk will focus on state-of-the-art deep learning studies in each face analysis problem after grouping the main approaches. Finally I conclude my talk with the challenges and future works on face analysis.