|Description (include details on usage, files and paper references)||POS Labeled Faces in the Wild, a collection of face which is proposed for studying face identification in unconstrained environment, its purpose is serving as a standard benchmark to evaluate the performance of various face recognition algorithms in real application. It contains more than 80,000 images of faces collected from the web. Each face has been annotated with the name of the person pictured. It has more than 3,300 subjects, each subject has more than 2 face images, which make it suitable for evaluating face recognition algorithms in an identification setting. The same with LFW dataset, our data set contains large variations such as pose, illumination, facial expression, age etc. All the faces were detected by the vector-boost based multi-view face detection algorithm.
Gary B. Huang, Marwan Mattar, Honglak Lee, and Erik Learned-Miller.
Learning to align from scratch.
In Neural Information Processing Systems (NIPS) , 2012.