Shejin Thavalengal

Iris authentication in handheld devices - considerations for constraint-free acquisition

As a near ideal biometric, iris authentication is widely used and mobile acquisition techniques are known. But iris acquisition on handheld imaging devices, such as smartphones, poses multiple, unique challenges. In this paper, a range of factors that affect the quality of iris images are reviewed. Iris size, image quality and acquisition wavelength are found to be key factors. Experimental results are presented confirming the lower limits of iris size for useful authentication performance. The authentication workflow for handheld devices is described. A case study on a current smartphone model is presented, including calculation of the pixel resolution that can be achieved with a visible-only optical system. Based on these analyses, system requirements for unconstrained acquisition in smartphones are discussed. Several design strategies are presented and key research challenges are outlined together with potential solutions.

Evaluation of combined visible/NIR camera for iris authentication on smartphones

Iris biometrics provide a mature and robust method of authentication, but are typically applied in a controlled environment and under constrained acquisition conditions. In this paper, the adaption of iris biometrics for unconstrained, hand-held use cases such as smartphones is investigated. A prototype optics-sensor combination is analysed in terms of its optical properties and iris imaging capabilities. The corresponding camera system with dual visible/NIR sensing capabilities and 4 Megapixel resolution is tested for suitability to implement iris recognition on smartphones. Recognition performance is analysed together with image quality comparisons. Preliminary results indicate that there are challenges to achieve reliable recognition performance in unconstrained use cases. Current optical systems are not diffraction limited, particularly at NIR wavelengths; pixel resolutions are close to the useful limits for iris recognition and acquisition conditions are challenging. Nevertheless, our findings indicate a similar camera module, with an improved optics and sensor, could combine biometric authentication with more conventional front-camera functions such as the capture of selfie images.

Feasibility and design considerations for an iris acquisition system for smartphones

Iris biometrics has the potential to provide the security required by next generation smartphones. This paper deals with the feasibility of an iris acquisition system for smartphones and similar hand held devices. When it comes to smartphones, a number of image acquisition challenges tend to surface. This paper discusses some of these challenges along with a brief description of acquisition system wavelength, iris image size and iris image spatial resolution and various other image quality parameters which affect iris recognition performance such as usable iris area, iris-pupil and iris-sclera contrast, image sharpness, gaze angle, SNR etc. Some preliminary results and design ideas for practical iris image acquisition are also presented.

Iris pattern obfuscation in digital images

As the imaging systems in handheld devices continue to improve, both in terms of optical quality and the use of advanced computational imaging techniques, we are close to a point where high quality iris images can be obtained from personal images, which in turn can be used for spoofing attacks against iris recognition system. Thus an emerging challenge for next-generation personal imaging devices is to provide a means to obfuscate iris pattern in digital photographs and videos, but without destroying the photo-realistic qualities of the eye regions in a photograph or video. This will effectively reduce the chance of obtaining iris patterns easily, which can be later used for spoofing. In this paper we propose five different techniques for iris pattern obfuscation and perform some initial testing to evaluate which are more robust. In addition some representative samples are provided of the visible effects on the appearance of eye regions.

Proof-of-concept and evaluation of a dual function visible/NIR camera for iris authentication in smartphones

As a robust method of person authentication, iris biometrics is making its way in to consumer devices such as smartphones. Current iris image acquisition devices typically work under controlled environment and constrained acquisition conditions. In this paper, the adaption of iris biometrics for unconstrained, hand-held devices such as smartphones is investigated. A prototype device is presented with full system description. This device is equipped with a single image sensor with both visible and NIR sensing capabilities. The device is analysed in terms of its optical properties and iris imaging capabilities. Preliminary results indicate that there are challenges to achieve a reliable recognition performance from the images captured using this device. Current system acquires images with marginal optical quality and spatial resolution in an unconstrained acquisition scenario for iris recognition. Nevertheless, the analyses presented in this paper indicate a similar camera module with improved optics and sensor could combine iris biometrics with conventional front camera functions such as video call and the capture of selfie images.

A realistic evaluation of iris presentation attack detection

Iris liveness detection methods have been developed to overcome the vulnerability of iris biometric systems to spoofing attacks. In the literature, it is typically assumed that a known attack modality will be perpetrated. Then liveness models are designed using labelled samples from both real/live and fake/spoof distributions, the latter derived from the assumed attack modality. In this work it is argued that a comprehensive modelling of the spoof samples is not possible in a real-world scenario where the attack modality cannot be known with a high degree of certainty. In fact making this assumption will render the liveness detection system more vulnerable to attacks that were not included in the original training. To provide a more realistic evaluation, this work proposes: a) testing the binary models with unknown spoof samples that were not present in the training step; b) the use of a single-class classification designing the classifier by modelling only the distribution of live samples. The results obtained support the assertion that many evaluation methods from the literature are misleading and may lead to optimistic estimates of the robustness of liveness detection in practical use cases.

Unconstrained palmprint as a smartphone biometric

In this work the use of palmprints as an alternative biometric for smartphones is investigated and its feasibility is evaluated across multiple devices using unconstrained acquisition. A novel multi-device database that covers a wide range of camera systems with data obtained in various conditions was built. This database contains palmprint images from 81 users acquired with 5 smartphones and is publicly available to the research community. Extensive experiments were carried out and their results placed into the context of existing, more constrained palmprint databases acquired with smartphones. The results outline the potential of palmprints to be used as an efficient secondary biometric deployed on consumer devices.

Efficient segmentation for multi-frame iris acquisition on smartphones

An iris segmentation technique, optimized for relatively low quality images acquired with smartphone visible-near infrared cameras is presented. The proposed technique is based on 1-D processing of the eye image and does not employ geometrical shape fitting. Hence, it is computationally efficient and enables multiple iris images to be extracted sequentially at native frame rates.

Iris recognition on consumer devices — Challenges and progress

This article outlines various technical, social and ethical challenges in implementing and widely adopting iris recognition technology on consumer devices such as smartphone or tablets. Acquisition of sufficient quality iris images using todays consumer devices is noted to be the main challenge in implementing this technology. Current progress in this field is reviewed. A smartphone form factor camera is presented to be used as a front-facing camera. This device is modified to capture near infra-red iris images along with general purpose visible wavelength images. Analyses shows that such a device with improved optics and sensor could be used for implementing iris recognition in next generation hand held devices. The social impact of wider adoption of this technology is discussed. Iris pattern obfuscation is presented to address various security and privacy concerns which may arise when iris recognition will be a part of our daily life.

An end to end Deep Neural Network for iris segmentation in unconstrained scenarios

With the increasing imaging and processing capabilities of todays mobile devices, user authentication using iris biometrics has become feasible. However, as the acquisition conditions become more unconstrained and as image quality is typically lower than dedicated iris acquisition systems, the accurate segmentation of iris regions is crucial for these devices. In this work, an end to end Fully Convolutional Deep Neural Network (FCDNN) design is proposed to perform the iris segmentation task for lower-quality iris images. The network design process is explained in detail, and the resulting network is trained and tuned using several large public iris datasets. A set of methods to generate and augment suitable lower quality iris images from the high-quality public databases are provided. The network is trained on Near InfraRed (NIR) images initially and later tuned on additional datasets derived from visible images. Comprehensive inter-database comparisons are provided together with results from a selection of experiments detailing the effects of different tunings of the network. Finally, the proposed model is compared with SegNet-basic, and a near-optimal tuning of the network is compared to a selection of other state-of-art iris segmentation algorithms. The results show very promising performance from the optimized Deep Neural Networks design when compared with state-of-art techniques applied to the same lower quality datasets.