Sequential Face Alignment via Person Specific Modeling in the Wild
Sequential face alignment, in essence, deals with nonrigid deformation that changes over time. Although numerous methods have been proposed to show impressive success on still images, many of them still suffer from limited performance when it comes to sequential alignment in wild scenarios, e.g. involving large pose/expression variations and partial occlusions. The underlying reason is that they usually perform sequential alignment by independently applying models trained offline in each frame in a tracking by detection manner but completely ignoring temporal constraints that become available in sequence. To address this issue, we propose to exploit incremental learning for person specific alignment. Our approach takes advantage of part based representation and cascade regression for robust and efficient alignment on each frame. More importantly, it incrementally updates the representation subspace and simultaneously adapts the cascade regressors in parallel using a unified framework. Person specific modeling is eventually achieved on the fly while the drifting issue is significantly alleviated by erroneous detection using both part and holistic descriptors. Extensive experiments on both controlled and in the wild datasets demonstrate the superior performance of our approach compared with the state of the arts in terms of fitting accuracy and efficiency.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)