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

Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-tracking signal. Compared to prior work, the network attains a lower error rate by one order of magnitude and is faster by two orders of magnitude: it identifies users accurately within seconds

Impact of BRCA1 and BRCA2 variants on splicing: clues from an allelic imbalance study

Nearly one-half of BRCA1 and BRCA2 sequence variations are variants of uncertain significance (VUSs) and are candidates for splice alterations for example, by disrupting/creating splice sites. As out-of-frame splicing defects lead to a marked reduction of the level of the mutant mRNA cleared through nonsense-mediated mRNA decay, a cDNA-based test was developed to show the resulting allelic imbalance (AI). Fifty-four VUSs identified in 53 hereditary breast/ovarian cancer (HBOC) patients without BRCA1/2 mutation were included in the study. Two frequent exonic single-nucleotide polymorphisms on both BRCA1 and BRCA2 were investigated by using a semiquantitative single-nucleotide primer extension approach and the cDNA allelic ratios obtained were corrected using genomic DNA ratios from the same sample. A total of five samples showed AI. Subsequent transcript analyses ruled out the implication of VUS on AI and identified a deletion encompassing BRCA2 exons 12 and 13 in one sample. No sequence abnormality was found in the remaining four samples, suggesting implication of cis- or trans-acting factors in allelic expression regulation that might be disease causative in these HBOC patients. Overall, this study showed that AI screening is a simple way to detect deleterious splicing defects and that a major role for VUSs and deep intronic mutations in splicing anomalies is unlikely in BRCA1/2 genes. Methods to analyze gene expression and identify regulatory elements in BRCA1/2 are now needed to complement standard approaches to mutational analysis