This page contains the download links for the source code for learning and computing the Fisher Vector Face (FVF) descriptor, described in . We also release an extensive set of pre-computed data packages, which can be used to exactly reproduce the results reported in . The FVF descriptors are learnt and evaluated on the Labeled Faces in the Wild (LFW) dataset . We release the learnt descriptor models and pre-computed descriptors for the following LFW evaluation settings: We investigate the problem of automatically labelling appearances of characters in TV or ï¬lm material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts, (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classiï¬er which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series â€˜â€˜Buffy the Vampire Slayerâ€. Source.