Periocular Region

About: Periocular Region is a research topic. Over the lifetime, 256 publications have been published within this topic receiving 4424 citations.

Spartans : Single-Sample Periocular-Based Alignment-Robust Recognition Technique Applied to Non-Frontal Scenarios

Abstract: 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.

Subspace-Based Discrete Transform Encoded Local Binary Patterns Representations for Robust Periocular Matching on NIST’s Face Recognition Grand Challenge

Abstract: 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.

Targeted therapy for orbital and periocular basal cell carcinoma and squamous cell carcinoma

Abstract: Purpose To review the literature on targeted therapy for orbital and periocular basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC) and provide examples of patients recently treated with such therapy. Methods The authors reviewed the literature on clinical results of targeted therapy and the molecular basis for targeted therapy in orbital and periocular BCC and cutaneous SCC. The authors also present representative cases from their practice. Results Mutation in the patched 1 gene (PTCH1) has been implicated in BCC, and overexpression of epidermal growth factor receptor (EGFR) has been shown in SCC. Vismodegib, an inhibitor of smoothened, which is activated upon binding of hedgehog to Ptc, has been shown to significantly decrease BCC tumor size or even produce complete resolution, especially in cases of basal cell nevus syndrome. Similarly, EGFR inhibitors have been shown to significantly decrease SCC tumor size in cases of locally advanced and metastatic disease. The authors describe successful outcomes after vismodegib treatment in a patient with basal cell nevus syndrome with numerous bulky lesions of the eyelid and periocular region and erlotinib (EGFR inhibitor) treatment in a patient with SCC who was deemed not to be a good surgical candidate because of advanced SCC of the orbit with metastasis to the regional lymph nodes, advanced age, and multiple medical comorbidities. Conclusions Targeted therapy using hedgehog pathway and EGFR inhibitors shows significant promise in treatment of orbital and periocular BCC and cutaneous SCC, respectively. Such targeted therapy may be appropriate for patients who are not good candidates for surgery.