726 research outputs found
Contact lens classification by using segmented lens boundary features
Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods
On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection
Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks.
In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems.
As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks
Object Detection and Classification in the Visible and Infrared Spectrums
The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition
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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