Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet

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Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocular trait does not have the high uniqueness presented in the iris trait. Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region. Also, to address the within-class variability caused by lighting and attributes in the periocular region, it is of paramount importance to use datasets with images of the same subject captured in distinct sessions. As the datasets available in the literature do not present all these factors, in this work, we present a new periocular dataset containing samples from 1,122 subjects, acquired in 3 sessions by 196 different mobile devices. The images were captured under unconstrained environments with just a single instruction to the participants: to place their eyes on a region of interest. We also performed an extensive benchmark with several Convolutional Neural Network (CNN) architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multitask Learning, Pairwise Filters Network, and Siamese Network. The results achieved in the closed- and open-world protocol, considering the identification and verification tasks, show that this area still needs research and development.

UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios.

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocular trait does not have the high uniqueness presented in the iris trait. Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region. Also, to address the within-class variability caused by lighting and attributes in the periocular region, it is of paramount importance to use datasets with images of the same subject captured in distinct sessions. As the datasets available in the literature do not present all these factors, in this work, we present a new periocular dataset containing samples from 1122 subjects, acquired in 3 sessions by 196 different mobile devices. The images were captured under unconstrained environments with just a single instruction to the participants: to place their eyes on a region of interest. We also performed an extensive benchmark with several Convolutional Neural Network (CNN) architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multi-task Learning, Pairwise Filters Network, and Siamese Network. The results achieved in the closed- and open-world protocol, considering the identification and verification tasks, show that this area still needs research and development. 

/pdf/a-new-periocular-dataset-collected-by-mobile-devices-in-3qucts43.pdf

A new periocular dataset collected by mobile devices in unconstrained scenarios

Periocular biometric, or peripheral area of ocular, is a collaborative alternative to face, especially if a face is occluded or masked. In practice, sole periocular biometric captures least salient facial features, thereby suffering from intra-class compactness and inter-class dispersion issues particularly in the wild environment. To address these problems, we transfer useful information from face to support periocular modality by means of knowledge distillation (KD) for embedding learning. However, applying typical KD techniques to heterogeneous modalities directly is suboptimal. We put forward in this paper a deep face-to-periocular distillation networks, coined as cross-modal consistent knowledge distillation (CM-CKD) henceforward. The three key ingredients of CM-CKD are (1) shared-weight networks, (2) consistent batch normalization, and (3) a bidirectional consistency distillation for face and periocular through an effectual CKD loss. To be more specific, we leverage face modality for periocular embedding learning, but only periocular images are targeted for identification or verification tasks. Extensive experiments on six constrained and unconstrained periocular datasets disclose that the CM-CKD-learned periocular embeddings extend identification and verification performance by 50% in terms of relative performance gain computed based upon face and periocular baselines. The experiments also reveal that the CM-CKD-learned periocular features enjoy better subject-wise cluster separation, thereby refining the overall accuracy performance.

Sapna S. Mishra

Papers

Variance-guided attention-based twin deep network for cross-spectral periocular recognition