A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf
.
Deep Learning Face Representation from Predicting 10,000 Classes
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted
doi:10.1109/cvpr.2014.244
dblp:conf/cvpr/SunWT14
fatcat:gdyfboylxjhs5of4dag6ckwznq