The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of singlebiometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

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We investigate if concentrative meditation training (CMT) offered during adolescent development benefits subsystems of attention using a quasi-experimental design. Attentional alerting, orienting, and conflict monitoring were examined using the Attention Network Test (ANT) in 13–15 year old children who received CMT as part of their school curriculum (CMT group: N = 79) vs. those who received no such training (control group: N = 76). Alerting and conflict monitoring, but not orienting, differed between the CMT and control group. Only conflict monitoring demonstrated age-related improvements, with smaller conflict effect scores in older vs. younger participants. The influence of CMT on this system was similar to the influence of developmental maturity, with smaller conflict effects in the CMT vs. control group. To examine if CMT might also bolster conflict-triggered upregulation of attentional control, conflict effects were evaluated as a function of previous trial conflict demands (high conflict vs. low conflict). Smaller current-trial conflict effects were observed when previous conflict was high vs. low, suggesting that similar to adults, when previous conflict was high (vs. low) children in this age-range proactively upregulated control so that subsequent trial performance was benefitted. The magnitude of conflict-triggered control upregulation was not bolstered by CMT but CMT did have an effect for current incongruent trials preceded by congruent trials. Thus, CMT's influence on attention may be tractable and specific; it may bolster attentional alerting, conflict monitoring and reactive control, but does not appear to improve orienting.

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A face image not only provides details about the identity of a subject but also reveals several attributes such as gender, race, sexual orientation, and age. Advancements in machine learning algorithms and popularity of sharing images on the World Wide Web, including social media websites, have increased the scope of data analytics and information profiling from photo collections. This poses a serious privacy threat for individuals who do not want to be profiled. This research presents a novel algorithm for anonymizing selective attributes which an individual does not want to share without affecting the visual quality of images. Using the proposed algorithm, a user can select single or multiple attributes to be surpassed while preserving identity information and visual content. The proposed adversarial perturbation based algorithm embeds imperceptible noise in an image such that attribute prediction algorithm for the selected attribute yields incorrect classification result, thereby preserving the information according to user's choice. Experiments on three popular databases i.e. MUCT, LFWcrop, and CelebA show that the proposed algorithm not only anonymizes k-attributes, but also preserves image quality and identity information.

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes random drop of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. In this research, we propose "guided dropout" for training deep neural network which drop nodes by measuring the strength of each node. We also demonstrate that conventional dropout is a specific case of the proposed guided dropout. Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of the proposed guided dropout.