A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

طبقات صخور الطفلة السوداء لعصر الأيوسين في تكوين" رس" بشمال سلطنة عمان - احتمال كونها صخور طفلة زيتية

A suite of samples from some black shale beds of lower Eocene Rus Formation from north of Oman have been investigated through detailed geochemical analyses. The obtained results indicate that these shale beds contain a significant amount of either soluble or insoluble organic matter. The total extractable dissolved organic matter content ranges from 3.5% to 5.8%, and Fischer Assay yields up to 20 U.S. gal/ton of oil. The average value of the total organic carbon content for these rocks is about 17.12% and that of kerogen content equals 27.21 %. The elemental analysis, vitrinite reflectances, IR-spectra, and TGA and DTG curves of kerogen isolates indicate that studied shales contain Types I and II kerogen which attained only an immature thermal maturation stage. The study reveals that Rus Formation black shale beds appear to have a good potential for shale oil production in north of Sultanate of Oman.تمت دراسة مجموعة من عينات صخور الطفلة السوداء المأخوذة من تكوين "رس" في شمال سلطنة عمان . وقد أوضحت نتائج الدراسة أن هذه الصخور تحتوي على نسبة عالية من المواد العضوية حيث تصل نسبة المواد البيتومينية بها إلى 5.8% ونسبة الكربون العضوي إلى 17.12% بينما تبلغ نسبة ما تحتويه من كيروجين حوالي 27.21 % ، كما أن هذه الصخور تعطي عند تحليلها بطريقة فيشر حوالي 20 جالون أمريكي من زيت البترول لكل طن . وأثبتت الدراسة أن صخور الطفلة تحتوي على كيروجين غني بالهيدروجين ويتبع النوع الأول والثاني غير الناضج . وخلصت الدراسة إلى أن طبقات الطفلة السوداء بالمنطقة تعتبر صخور طفلة زيتية غنية بالمواد العضوية ولها جهد عال لإنتاج المواد الهيدروكربونية