Joint Face Detection and Alignment with a Deformable Hough Transform Model

Abstract

We propose a method for joint face detection and alignment in unconstrained images and videos. Historically, these problems have been addressed disjointly in literature with the overall performance of the whole pipeline having been scantily assessed. We show that a pipeline built by combining state-of-the-art methods for both tasks produces unsatisfactory overall performance. To address this limitation, we propose an approach that addresses both tasks, which we call Deformable Hough Transform Model (DHTM). In particular, we make the following contributions: (a) Rather than scanning the image with discriminatively trained filters, we propose to employ cascaded regression in a sliding window fashion to fit a facial deformable model over the whole image/video. (b) We propose to capitalize on the large basin of attraction of cascaded regression to set up a Hough-Transform voting scheme for detecting faces and filtering out irrelevant background. (c) We report state-of-the-art performance on the most challenging and widely-used data sets for face detection, alignment and tracking.