This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: Iri-Core, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5 for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5 was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.
First publicly available databases to present iris images obtained in visible light were the UPOL @cite_18 , UBIRISv1 @cite_17 and UBIRISv2 @cite_2 datasets. The first one contains images collected with an ophthalmology device, the second - scaled down photographs taken with Nikon E5700 handheld camera (resampling was introduced to simulated poor quality of unconstrained imaging), and the third - images captured and , also in visible light. Recently, a dataset of iris images and iris image printouts ( fake' iris images) obtained using various mobile devices has been released @cite_3 , however, these are also images taken without built-in flash illumination.
@cite_18 @cite_2 @cite_3 @cite_17
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The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition. Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36 and rate of accepted spoof samples of 14.71 . The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions. Presentation attacks such as printed iris images or patterned contact lenses can be used to circumvent an iris recognition system. Different solutions have been proposed to counteract this vulnerability with Presentation Attack Detection (commonly called liveness detection) being used to detect the presence of an attack, yet independent evaluations and comparisons are rare. To fill this gap we have launched the first international iris liveness competition in 2013. This paper presents detailed results of its second edition, organized in 2015 (LivDet-Iris 2015). Four software-based approaches to Presentation Attack Detection were submitted. Results were tallied across three different iris datasets using a standardized testing protocol and large quantities of live and spoof iris images. The Federico Algorithm received the best results with a rate of rejected live samples of 1.68 and rate of accepted spoof samples of 5.48 . This shows that simple static attacks based on paper printouts and printed contact lenses are still challenging to be recognized purely by software-based approaches. Similar to the 2013 edition, printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses. Iris is one of the preferred biometric modalities. Nevertheless, the focus of iris image has to be good enough to achieve good recognition performance. Traditional iris imaging devices in the visible spectrum suffer from limited depth-of-field which results in out-of-focus iris images. The acquisition of iris image is thus repeated until a satisfactory focus is obtained or the image is post-processed to improve the visibility of texture pattern. Bad focused images obtained due to non-optimal focus degrade the identification rate. In this work, we propose a novel scheme to capture high quality iris samples by exploring new sensors based on light-field technology to address the limited depth-of-field exhibited by the conventional iris sensors. The idea stems out from the availability of multiple depth focus images in a single exposure. We propose to use the best-focused iris image from the set of depth images rendered by the Light-field Camera (LFC). We further evaluate the proposed scheme experimentally with a unique and newly acquired iris database simulating the real-life scenario.