189 research outputs found
Evaluating soft biometrics in the context of face recognition
2013 Summer.Includes bibliographical references.Soft biometrics typically refer to attributes of people such as their gender, the shape of their head, the color of their hair, etc. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. Here four experiments quantify performance gains on a difficult face recognition task when standard face recognition algorithms are augmented using information associated with soft biometrics. These experiments include a best-case analysis using perfect knowledge of gender and race, support vector machine-based soft biometric classifiers, face shape expressed through an active shape model, and finally appearance information from the image region directly surrounding the face. All four experiments indicate small improvements may be made when soft biometrics augment an existing algorithm. However, in all cases, the gains were modest. In the context of face recognition, empirical evidence suggests that significant gains using soft biometrics are hard to come by
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 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
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