Search

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.

In this paper, we investigate a single-sample periocular-based alignment-robust face recognition technique that is pose-tolerant under unconstrained face matching scenarios. Our Spartans framework starts by utilizing one single sample per subject class, and generate new face images under a wide range of 3D rotations using the 3D generic elastic model which is both accurate and computationally economic. Then, we focus on the periocular region where the most stable and discriminant features on human faces are retained, and marginalize out the regions beyond the periocular region since they are more susceptible to expression variations and occlusions. A novel facial descriptor, high-dimensional Walsh local binary patterns, is uniformly sampled on facial images with robustness toward alignment. During the learning stage, subject-dependent advanced correlation filters are learned for pose-tolerant non-linear subspace modeling in kernel feature space followed by a coupled max-pooling mechanism which further improve the performance. Given any unconstrained unseen face image, the Spartans can produce a highly discriminative matching score, thus achieving high verification rate. We have evaluated our method on the challenging Labeled Faces in the Wild database and solidly outperformed the state-of-the-art algorithms under four evaluation protocols with a high accuracy of 89.69%, a top score among image-restricted and unsupervised protocols. The advancement of Spartans is also proven in the Face Recognition Grand Challenge and Multi-PIE databases. In addition, our learning method based on advanced correlation filters is much more effective, in terms of learning subject-dependent pose-tolerant subspaces, compared with many well-established subspace methods in both linear and non-linear cases.

In this paper, we employ several subspace representations (principal component analysis, unsupervised discriminant projection, kernel class-dependence feature analysis, and kernel discriminant analysis) on our proposd discrete transform encoded local binary patterns (DT-LBP) to match periocular region on a large data set such as NIST's face recognition grand challenge (FRGC) ver2 database. We strictly follow FRGC Experiment 4 protocol, which involves 1-to-1 matching of 8014 uncontrolled probe periocular images to 16 028 controlled target periocular images (~128 million pairwise face match comparisons). The performance of the periocular region is compared with that of full face with different illumination preprocessing schemes. The verification results on periocular region show that subspace representation on DT-LBP outperforms LBP significantly and gains a giant leap from traditional subspace representation on raw pixel intensity. Additionally, our proposed approach using only the periocular region is almost as good as full face with only 2.5% reduction in verification rate at 0.1% false accept rate, yet we gain tolerance to expression, occlusion, and capability of matching partial faces in crowds. In addition, we have compared the best standalone DT-LBP descriptor with eight other state-of-the-art descriptors for facial recognition and achieved the best performance. The two general frameworks are our major contribution: 1) a general framework that employs various generative and discriminative subspace modeling techniques for DT-LBP representation and 2) a general framework that encodes discrete transforms with local binary patterns for the creation of robust descriptors.

Robust facial hair detection and segmentation is a highly valued soft biometric attribute for carrying out forensic facial analysis. In this paper, we propose a novel and fully automatic system, called SparCLeS, for beard/moustache detection and segmentation in challenging facial images. SparCLeS uses the multiscale self-quotient (MSQ) algorithm to preprocess facial images and deal with illumination variation. Histogram of oriented gradients (HOG) features are extracted from the preprocessed images and a dynamic sparse classifier is built using these features to classify a facial region as either containing skin or facial hair. A level set based approach, which makes use of the advantages of both global and local information, is then used to segment the regions of a face containing facial hair. Experimental results demonstrate the effectiveness of our proposed system in detecting and segmenting facial hair regions in images drawn from three databases, i.e., the NIST Multiple Biometric Grand Challenge (MBGC) still face database, the NIST Color Facial Recognition Technology FERET database, and the Labeled Faces in the Wild (LFW) database.