Frontal to profile face verification in the wild
We have collected a new face data set that will facilitate research in the problem of frontal to profile face verification `in the wild' The aim of this data set is to isolate the factor of pose variation in terms of extreme poses like profile, where many features are occluded, along with other `in the wild' variations. We call this data set the Celebrities in Frontal Profile (CFP) data set. We find that human performance on Frontal Profile verification in this data set is only slightly worse (94.57% accuracy) than that on Frontal Frontal verification (96.24% accuracy) However we evaluated many state of the art algorithms, including Fisher Vector, Sub SML and a Deep learning algorithm. We observe that all of them degrade more than 10% from Frontal Frontal to Frontal Profile verification. The Deep learning implementation, which performs comparable to humans on Frontal Frontal, performs significantly worse (84.91% accuracy) on Frontal Profile. This suggests that there is a gap between human performance and automatic face recognition methods for large pose variation in unconstrained images.
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)