This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses. This software can serve as a baseline in various PAD evaluations, and also as an open-source platform with an up-to-date reference method for iris PAD. The software is written in C++ and Python and uses only open source resources, such as OpenCV. This method does not incorporate iris image segmentation, which may be problematic for unknown fake samples. Instead, it makes a best guess to localize the rough position of the iris. The PAD-related features are extracted with the Binary Statistical Image Features (BSIF), which are classified by an ensemble of classifiers incorporating support vector machine, random forest and multilayer perceptron. The models attached to the current software have been trained with the NDCLD'15 database and evaluated on the independent datasets included in the LivDet-Iris 2017 competition. The software implements the functionality of retraining the classifiers with any database of authentic and attack images. The accuracy of the current version offered with this paper exceeds 99 when tested on subject-disjoint subsets of NDCLD'15, and oscillates around 85 when tested on the LivDet-Iris 2017 benchmarks, which is on par with the results obtained by the LivDet-Iris 2017 winner.
In the context of the existing tools for and efforts towards faster development of iris PAD methodologies, it is worth mentioning numerous benchmark databases, such as Clarkson , Warsaw , Notre Dame , and WVU IIITD-Delhi developed for LivDet-Iris competitions @cite_9 @cite_6 @cite_0 (paper printouts and textured contact lenses), NDCCL 2012 @cite_4 , NDCLD 2013 , @cite_20 and NDCLD 2015 @cite_10 (cleat and textured contact lenses), ATVS-FIr @cite_8 (paper printouts), Pupil-Dynamics @cite_2 (pupil size in time with and without visible-light stimuli), Post-Mortem-Iris @cite_18 (images of irises acquired up to one month after death), CASIA-Iris-Syn @cite_1 (synthetically generated iris images), or data acquired by a LightField sensor GUC-LF-VIAr-DB @cite_16 . The LivDet-Iris competitions http: livdet.org , mentioned earlier, are an important effort towards independent evaluation of iris PAD algorithms. Editions were organized in 2013 @cite_9 , 2015 @cite_6 , and 2017 @cite_0 and brought together researchers from around the world, who submitted their iris PAD algorithms for evaluation.
@cite_18 @cite_4 @cite_8 @cite_9 @cite_1 @cite_6 @cite_0 @cite_2 @cite_16 @cite_10 @cite_20
2787293095 2627044814 2016957240 2005411195
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. It is necessary to match heterogeneous iris images captured by different types of iris sensors with an increasing demand of interoperable identity management systems. The significant differences among multiple types of iris sensors such as optical lens and illumination wavelength determine the cross-sensor variations of iris texture patterns. Therefore it is a challenging problem to select the common feature set which is effective for all types of iris sensors. This paper proposes a novel optimization model of coupled feature selection for cross-sensor iris recognition. The objective function of our model includes two parts: the first part aims to minimize the misclassification errors; the second part is designed to achieve sparsity in coupled feature spaces based on l2,1-norm regularization. In the training stage, the proposed feature selection model can be formulated as a half-quadratic optimization problem, where an iterative algorithm is developed to obtain the solution. Experimental results on the Notre Dame Cross Sensor Iris Database and CASIA cross sensor iris database show that features selected by the proposed method perform better than those selected by conventional single-space feature selection methods such as Boosting and h regularization methods. Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3 , 14.5 , 16.3 , 13.5 , and 10.3 penetration rates and 98.2 , 91.1 , 90.7 , 85.2 , and 96 hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases.