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

Abstract

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-ofthe-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.

Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

“…For the CW protocol, PeriGender performed better than Zanlorensi et al's method [19] for the whole periocular region on the UFPR dataset. We validated the model on all UFPR images without omitting any existing images.…”

“…Previous studies [9][10]14,16,21] have performed 5-fold cross validation (CV) for the OW protocol. The classification performance in these studies was lower than that of other studies implemented in the CW protocol [16,19]. Reference [11] did not indicate whether a validation protocol was used for an OW or a CW scenario.…”

“…Until 2020, there was a lack of challenging wild biometric databases with gender metadata. Zanlorensi et al [19] launched an unconstrained large-scale UFPR dataset that is useful for investigating our research problem. The dataset features images with different illumination, resolution, and occlusion conditions.…”

“…6. In the UFPR, we applied the CW and OW protocols specified in [19]. We followed the original subject-disjoint protocol from [42] for the Ethnic-Ocular dataset.…”

“…GC using the periocular region has received extensive research interest [9][10][11][12][13][14][15][16][17][18][19][20][21]. Some studies have employed handcrafted features [9][10][11][12][13], and others have used deep learning to complete the GC task [14][15][16][17][18][19][20][21]. However, most methods were evaluated using images captured under constrained settings.…”