IEEE Trans Pattern Anal Mach Intell - Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics.

Tópicos

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Resumo

Computer vision systems have demonstrated considerable improvement in recognizing and verifying faces in digital images. Still, recognizing faces appearing in unconstrained, natural conditions remains a challenging task. In this paper, we present a face-image, pair-matching approach primarily developed and tested on the "Labeled Faces in the Wild" (LFW) benchmark that reflects the challenges of face recognition from unconstrained images. The approach we propose makes the following contributions. 1) We present a family of novel face-image descriptors designed to capture statistics of local patch similarities. 2) We demonstrate how unlabeled background samples may be used to better evaluate image similarities. To this end, we describe a number of novel, effective similarity measures. 3) We show how labeled background samples, when available, may further improve classification performance, by employing a unique pair-matching pipeline. We present state-of-the-art results on the LFW pair-matching benchmarks. In addition, we show our system to be well suited for multilabel face classification (recognition) problem, on both the LFW images and on images from the laboratory controlled multi-PIE database.

Resumo Limpo

comput vision system demonstr consider improv recogn verifi face digit imag still recogn face appear unconstrain natur condit remain challeng task paper present faceimag pairmatch approach primarili develop test label face wild lfw benchmark reflect challeng face recognit unconstrain imag approach propos make follow contribut present famili novel faceimag descriptor design captur statist local patch similar demonstr unlabel background sampl may use better evalu imag similar end describ number novel effect similar measur show label background sampl avail may improv classif perform employ uniqu pairmatch pipelin present stateoftheart result lfw pairmatch benchmark addit show system well suit multilabel face classif recognit problem lfw imag imag laboratori control multipi databas

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