Development of Robust Iris Localization and Impairment Pruning Schemes

Iris is the sphincter having flowery pattern around pupil in the eye region. The high randomness of the pattern makes iris unique for each individual and iris is identified by the scientists to be a candidate for automated machine recognition of identity of an individual. The morphogenesis of iris is completed while baby is in mother's womb; hence the iris pattern does not change throughout the span of life of a person. It makes iris one of the most reliable biometric traits. Localization of iris is the first step in iris biometric recognition system. The performance of matching is dependent on the accuracy of localization, because mislocalization would lead the next phases of biometric system to malfunction. The first part of the thesis investigates choke points of the existing localization approaches and proposes a method of devising an adaptive threshold of binarization for pupil detection. The thesis also contributes in modifying conventional integrodifferential operator based iris detection and proposes a modified version of it that uses canny detected edge map for iris detection. The other part of the thesis looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like Scale Invariant Feature Transform(SIFT) gives satisfactory recognition accuracy for good quality images. But the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The thesis proposes two methods of filtering impairments (faulty pairs) based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms (Angular Filtering and Scale Filtering) are applied separately and applied in union to have a complete comparative analysis of the result of matching

A Real-time Model for Multiple Human Face Tracking from Low-resolution Surveillance Videos

AbstractThis article discusses a novel approach of multiple-face tracking from low-resolution surveillance videos. There has been significant research in the field of face detection using neural-network based training. Neural network based face detection methods are highly accurate, albeit computationally intensive. Hence neural network based approaches are not suitable for real-time applications. The proposed approach approximately detects faces in an image solely using the color information. It detects skin region in an image and finds existence of eye and mouth region in the skin region. If it finds so, it marks the skin region as a face and fits an oriented rectangle to the face. The approach requires low computation and hence can be applied on subsequent frames from a video. The proposed approach is tested on FERET face database images, on different images containing multiple faces captured in unconstrained environments, and on frames extracted from IP surveillance camera

Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities

Visual surveillance is the latest paradigm for social security through machine intelligence. It includes the use of visual data captured by infrared sensors or visible-light cameras mounted in cars, corridors, traffic signals etc. Visual surveillance facilitates the classification of human behavior, crowd activity, and gesture analysis to achieve application-specific objectivesinfo:eu-repo/semantics/publishedVersio

Palmprint identification using an ensemble of sparse representations

Among various palmprint identification methods proposed in the literature, sparse representation for classification (SRC) is very attractive offering high accuracy. Although SRC has good discriminative ability, its performance strongly depends on the quality of the training data. In particular, SRC suffers from two major problems: lack of training samples per class and large intra-class variations. In fact, palmprint images not only contain identity information but they also have other information, such as illumination and geometrical distortions due to the unconstrained conditions and the movement of the hand. In this case, the sparse representation assumption may not hold well in the original space since samples from different classes may be considered from the same class. This paper aims to enhance palmprint identification performance through SRC by proposing a simple yet efficient method based on an ensemble of sparse representations through an ensemble of discriminative dictionaries satisfying SRC assumption. The ensemble learning has the advantage to reduce the sensitivity due to the limited size of the training data and is performed based on random subspace sampling over 2D-PCA space while keeping the image inherent structure and information. In order to obtain discriminative dictionaries satisfying SRC assumption, a new space is learned by minimizing and maximizing the intra-class and inter-class variations using 2D-LDA. Extensive experiments are conducted on two publicly available palmprint data sets: multispectral and PolyU. Obtained results showed very promising results compared with both state-of-the-art holistic and coding methods. Besides these findings, we provide an empirical analysis of the parameters involved in the proposed technique to guide the neophyte. 2018 IEEE.This work was supported by the National Priority Research Program from the Qatar National Research Fund under Grant 6-249-1-053. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.Scopu

Introduction to special issue on ‘Recent computing paradigms, network protocols, and applications’

This special issue of Innovations in Systems and Software Engineering: A NASA Journal is devoted to selected contributions from the 3rd International Conference on Advanced Computing, Networking and Informatics (ICACNI-2015), organized by School of Computer Engineering, KIIT University, Odisha, India, during 23–25 June, 2015. The conference commenced with a keynote by Prof. Nikhil R. Pal (Fellow IEEE, Vice President for Publications IEEE Computational Intelligence Society (2015–2016), Indian Statistical Institute, Kolkata, India) on ‘A Fuzzy Rule-Based Approach to Single Frame Super Resolution’. Apart from three regular tracks on advanced computing, networking, and informatics, the conference hosted three invited special sessions. While a total of 558 articles across different tracks of the conference were received, 132 articles are finally selected for presentation and publication by Smart Innovation, Systems and Technologies series of Springer as Volume 43 and 44. The conference showcased a technical talk by Prof. Nabendu Chaki (Senior Member IEEE, Calcutta University, India) on ‘Evolution from Web-based Applications to Cloud Services: A Case Study with Remote Healthcare’. The conference identified some wonderful works and has given away eight awards in different categories

GAMap : A Genetic Algorithm based Effective Virtual Data Center Re-Embedding Strategy

Network virtualization allows the service providers (SPs) to divide the substrate resources into isolated entities called virtual data centers (VDCs). Typically, a VDC comprises multiple cooperative virtual machines (VMs) and virtual links (VLs) capturing their communication relationships. The SPs often re-embed VDCs entirely or partially to meet dynamic resource demands, balance the load, and perform routine maintenance activities. This paper proposes a genetic algorithm (GA)-based effective VDC re-embedding (GAMap) framework that focuses on a use case where the SPs relocate the VDCs to meet their excess resource demands, introducing the following challenges. Firstly, it encompasses the re-embedding of VMs. Secondly, VL re-embedding follows the re-embedding of the VMs, which adds to the complexity. Thirdly, VM and VL re-embedding are computationally intractable problems and are proven to be NP-Hard. Given these challenges, we adopt the GA-based solution that generates an efficient re-embedding plan with minimum costs. Experimental evaluations confirm that the proposed scheme shows promising performance by achieving an 11.94% reduction in the re-embedding cost compared to the baselines

Introduction to special issue on intelligent computing and adaptive systems

This special issue of Innovations in Systems and Software Engineering: A NASA Journal is devoted to selected contributions from the 4th International Conference on Advanced Computing, Networking and Informatics (ICACNI 2016

Biometrics: Accessibility challenge or opportunity?

Biometric recognition is currently implemented in several authentication contexts, most recently in mobile devices where it is expected to complement or even replace traditional authentication modalities such as PIN (Personal Identification Number) or passwords. The assumed convenience characteristics of biometrics are transparency, reliability and ease of use, however, the question of whether biometric recognition is as intuitive and straightforward to use is open to debate. Can biometric systems make some tasks easier for people with accessibility concerns? To investigate this question, an accessibility evaluation of a mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated Teller Machine) scenario. The biometric authentication mechanisms used include face, voice, and fingerprint. Furthermore, we employed traditional modalities of PIN and pattern in order to check if biometric recognition is indeed a real improvement. The trial test subjects within this work were people with real-life accessibility concerns. A group of people without accessibility concerns also participated, providing a baseline performance. Experimental results are presented concerning performance, HCI (Human-Computer Interaction) and accessibility, grouped according to category of accessibility concern. Our results reveal links between individual modalities and user category establishing guidelines for future accessible biometric products