This paper presents an unsupervised learning approach to video-based face recognition that does not make any assumptions about the pose, expressions or prior localization of landm...
Abstract. Heterogeneous face images come from different lighting conditions or different imaging devices, such as visible light (VIS) and near infrared (NIR) based. Because heter...
ShengCai Liao, Dong Yi, Zhen Lei, Rui Qin, Stan Z....
We present a robust elastic and partial matching metric
for face recognition. To handle challenges such as pose, facial
expression and partial occlusion, we enable both elastic
...
We present a novel face recognition method using automatically extracted sketch by a multi-layer grammatical face model. First, the observed face is parsed into a 3layer (face, pa...
In this paper, we propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. The approa...