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Variational Level Set (LS) has been a widely used method in medical segmentation. However, it is limited when dealing with multi-instance objects in the real world. In addition, its segmentation results are quite sensitive to initial settings and highly depend on the number of iterations. To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS) 1 to employ Gated Recurrent Unit under the energy minimization of a variational LS functional. The curve deformation process in RLS is formed as a hidden state evolution procedure and updated by minimizing an energy functional composed of fitting forces and contour length. By sharing the convolutional features in a fully end-to-end trainable framework, we extend RLS to Contextual RLS (CRLS) to address semantic segmentation in the wild. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational LS-based method whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches.

Recent studies in biometrics have shown that the peri ocular region of the face is sufficiently discriminative for robust recognition, and particularly effective in certain sce narios such as extreme occlusions, and illumination vari ations where traditional face recognition systems are un reliable. In this paper, we first propose a fully automatic, robust and fast graph-cut based eyebrow segmentation tech nique to extract the eyebrow shape from a given face image. We then propose an eyebrow shape-based identification sys tem for periocular face recognition. Our experiments have been conducted over large datasets from the MBGC and AR databases and the resilience of the proposed approach has been evaluated under varying data conditions. The exper imental results show that the proposed eyebrow segmenta tion achieves high accuracy with an F-Measure of 99.4% and the identification system achieves rates of 76. 0% on the AR database and 85.0% on the MBGC database.

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