Mobile or IOT based applications are emerging rapidly across the globe and there is a massive digital transformation happening within each country. It is a need of an hour to improve and protect digital identity during online transactions through handheld devices. This paper proposes a Mobile ID solution based on Mobile-originated PKI without the need for the actual identity card or a card reader. The solution proposed focuses on security, privacy, and usability using open standards which will protect Personally Identifiable Information (PII) over handheld devices. The proposed mobile ID solution has better cost-efficacy and privacy than today’s scenario. It also explicates the Mobile ID solution with established secure identity among users, authorities, other organizations of public, and private sectors.

In multimodal biometric systems, human identification is performed by fusing information in different ways like sensor-level, feature-level, score-level, rank-level and decision-level. Score-level fusion is preferred over other levels of fusion because of its low complexity and sufficient availability of information for fusion. However, the scores obtained from different unimodal systems are heterogeneous in nature and hence they all require normalization before fusion. In this paper, we propose a clientcentric score normalization technique based on extreme value theory (EVT), exploiting the properties of Generalized Extreme Value (GEV) distribution. The novelty lies in the application of extreme value theory over the tail of the complete score distribution (genuine and impostor scores), assuming that the genuine scores form extreme values (tail) with respect to the entire set of scores. Normalization is then performed by estimating the cumulative density function of GEV distribution, using the parameter set obtained from genuine data. For evaluation, the proposed method is compared with state-of-the-art methods on two publicly available multimodal databases: i) NIST BSSR1 [22] multimodal biometric score database and ii) Database created from Face Recognition Grand Challenge V2.0 [23] and LG4000 iris images [24], to show the efficiency of the proposed method.

Availability of a single training sample (STS) or degraded set (DS) of training and testing samples restricts the success of face recognition in real-world applications. We propose a unified framework for handling both these challenges simultaneously by using a data dictionary, which is a combination of training dictionary and intra-class variation dictionary. The training dictionary is assembled by the single representative sample per class. Variations between the training samples and a query image are captured by the intra-class variation dictionary. Misalignment of the query image is handled by aligning it with respect to the representative samples. A few moderately aligned and warped face images obtained from the query image are then sparsely represented using the data dictionary with additional constraints on their variance which reduces the obligation of a perfectly aligned query image. The experiments results on AR and LFW datasets validate our claim of superior performance in STS and DS as compared to the other recent methods.

Weyuker's Property 9 has received a mixed response regarding its applicability to object oriented software metrics. Contrary to past beliefs, the relevance of this property to object oriented systems is brought out. In support of the new argument, counterexamples to earlier claims are formulated and two new metrics highlighting a notion of complexity that is capturable through Property 9 are also presented.