Enhanced iris recognition: Algorithms for segmentation, matching and synthesis

This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique

Deteksi Iris Berdasarkan Metode Black Hole Dan Circle Curve Fitting

Sistem pengenalan identitas personal berdasarkan ciri biometrika adalah suatu sistem pengenalan seseorang berdasarkan pada ciri biometrika yang melekat pada orang tersebut. Iris mata merupakan salah satu ciri biometrik yang handal untuk sistem pengenalan identitas personal. Bagian sistem pengenalan identitas personal berdasarkan biometrik iris yang dianggap paling krusial adalah deteksi lokasi iris, karena akurasi deteksi iris berpengaruh pada tingkat akurasi sistem secara keseluruhan. Lokasi iris pada citra mata dibatasi oleh dua buah lingkaran yang memisahkan antara bagian iris  dengan pupil dan sklera. Telah banyak metodemetode yang diusulkan oleh para peneliti untuk menghasilkan deteksi lokasi iris dengan akurat dan cepat. Masalah akurasi, kecepatan waktu eksekusi dan ketahanan terhadap noise merupakan bidang penelitian yang menantang pada deteksi iris. Makalah ini menyajikan metode deteksi iris menggunakan metode black hole dan circle curve fitting. Langkah pertama, mencari batas dalam lingkaran iris yang memisahkan antara daerah iris dan pupil. Dengan metode black hole yang bekerja berdasarkan fakta bahwa lokasi pupil merupakan daerah  lingkaran yang paling hitam dan memiliki distribusi nilai intensitas yang seragam, maka lokasi pupil dapat ditentukan dengan teknik pengambangan. Batas lingkaran pupil dapat ditentukan dengan circle curve fitting dari parameter lingkaran daerah pupil. Langkah kedua,  mencari batas luar lingkaran iris yang memisahkan antara iris dan sklera. Peta tepi citra iris dicari dengan menggunakan deteksi tepi Canny, kemudian diambil satu komponen tepi arah vertikal yang dapat mewakili batas lingkaran luar iris. Dari komponen tepi tersebut, dihitung jari-jari iris yang berpusat di pusat pupil. Dengan jari-jari iris dan pusat iris maka dapat ditentukan batas luar iris menggunakan circle curve fittin

Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end a combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 201

An Optokinetic Nystagmus Detection Method for Use With Young Children

Sangi, M., Thompson, B., & Turuwhenua, J. (2015). An Optokinetic Nystagmus Detection Method for Use With Young Children. IEEE Journal of Translational Engineering in Health and Medicine, 3, 1600110. http://doi.org/10.1109/JTEHM.2015.2410286 ©IEEEThe detection of vision problems in early childhood can prevent neurodevelopmental disorders such as amblyopia. However, accurate clinical assessment of visual function in young children is challenging. optokinetic nystagmus (OKN) is a reflexive sawtooth motion of the eye that occurs in response to drifting stimuli, that may allow for objective measurement of visual function in young children if appropriate child-friendly eye tracking techniques are available. In this paper, we present offline tools to detect the presence and direction of the optokinetic reflex in children using consumer grade video equipment. Our methods are tested on video footage of children (N = 5 children and 20 trials) taken as they freely observed visual stimuli that induced horizontal OKN. Using results from an experienced observer as a baseline, we found the sensitivity and specificity of our OKN detection method to be 89.13% and 98.54%, respectively, across all trials. Our OKN detection results also compared well (85%) with results obtained from a clinically trained assessor. In conclusion, our results suggest that OKN presence and direction can be measured objectively in children using consumer grade equipment, and readily implementable algorithms

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets