אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Recent methods for learning the similarities between images have presented impressive results on the problem of pair-matching (same/not-same classification) of face images. In this talk we present pair-matching results comparing the performance of image descriptor based methods to the state of the art in same/not-same classification, obtained on the Labeled Faces in the Wild (LFW) image set. We propose various contributions, spanning several aspects of automatic face analysis: (i) We present a family of novel image descriptors which we call the "patch-LBP" descriptors. (ii) We show that descriptor based methods can obtain performance which is comparable to existing state of the art methods on both the same/not-same and multi-person recognition problems. (iii) We present the novel "One-Shot" vector similarity measure which we have used to improve our same/not-same results well above leading methods.

* Joint work with Lior Wolf (TAU) and Yaniv Taigman (TAU and face.com)