Padma Polash Paul

Bio: Padma Polash Paul is an academic researcher from University of Calgary. The author has contributed to research in topics: Biometrics & Facial recognition system. The author has an hindex of 14, co-authored 41 publications receiving 610 citations. Previous affiliations of Padma Polash Paul include City University of Hong Kong & University of Oxford.

DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect

Abstract: This paper presents a new 3D gait recognition method that utilizes the kinect skeleton data for representing the gait signature. We propose to use two new features, namely joint relative distance (JRD) and joint relative angle (JRA), which are robust against view and pose variations. The relevance of each JRD and JRA sequence in representing human gait is evaluated using a genetic algorithm. We also introduce a dynamic time warping-based kernel that takes a collection of JRD or JRA sequences as parameters and computes a dissimilarity measure between the training and the unknown sample. The proposed kernel can effectively handle variable walking speed without any need of extra pre-processing. In addition, we propose a rank-level fusion of JRD and JRA features that can boost the overall recognition performance greatly. The effectiveness of the proposed method is evaluated using a 3D skeletal gait database captured with a Kinect v2 sensor. In our experiments, rank level fusion of joint relative distance (JRD) and joint relative angle (JRA) achieves promising results, as compared against only JRD and only JRA-based gait recognition.

Multimodal Cancelable Biometrics

Abstract: Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains. It has to do with actual biometric characteristics of users being permanent, and their number being limited. Thus, if user's biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. Cancellable biometric for individual biometric has been a significantly understudied problem. The concept of cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that users can change their single biometric template in a biometric security system. However, cancelability in multimodal biometric has been barely addressed at all. In this paper, we tackle the problem and present a novel solution for cancelable biometrics in multimodal system. We develop a new cancelable biometric template generation algorithm using random projection and transformation-based feature extraction and selection. Performance of the proposed algorithm is validated on multi-modal face and ear database.

Decision Fusion for Multimodal Biometrics Using Social Network Analysis

Abstract: This paper presents for the first time decision fusion for multimodal biometric system using social network analysis (SNA) The main challenge in the design of biometric systems, at present, lies in unavailability of high-quality data to ensure consistently high recognition results Resorting to multimodal biometric partially solves the problem, however, issues with dimensionality reduction, classifier selection, and aggregated decision making remain The presented methodology successfully overcomes the problem through employing novel decision fusion using SNA While several types of feature extractors can be used to reduce the dimension and identify significant features, we chose the Fisher Linear Discriminant Analysis as one of the most efficient methods Social networks are constructed based on similarity and correlation of features among the classes The final classification result is generated based on the two levels of decision fusion methods At the first level, individual biometrics (face or ear or signature) are classified using matching score methodology SNA is used to reinforce the confidence level of the classifier to reduce the error rate In the second level, outcomes of classification based on individual biometrics are fused together to obtain the final decision

Kinect Sensor Gesture and Activity Recognition: New Applications for Consumer Cognitive Systems

Abstract: Cognitive consumer electronics (CE), the fastest-growing sector worldwide that is driven by machine intelligence and cognitive systems, is triggered and enabled by audio- and video-capturing devices, smart sensors, health- and fitness-monitoring devices, security and education electronics, and intelligent systems. Smart consumer sensors and cognitive systems are synergized through the Internet of Things (IoT) for optimal information sharing, communication, real-time updates, data analytics, and enhanced support for decision making. Biometric-based devices, originally intended for large-scale applications in airports, border controls, disaster zones, or refugee migration zones, are enabling a wide range of applications in commercial and consumer sectors as standalone systems or with interconnected sensor networks. This article introduces the applications of Microsoft Kinect in cognitive systems for smart CE, and, using Kinect sensors, a human-behavior cognition technology is presented for gesture and activity recognition. As a novel front end of pervasive cognitive systems, the challenges and applications of a Kinect sensor-based system will be explored in CE, such as smart automobiles, health care, surveillance, and activity recognition.

A Concept of Social Behavioral Biometrics: Motivation, Current Developments, and Future Trends

Abstract: A person can be identified from his physiological traits as well as from behavioral patterns. However, a person's behavior is not only confined to individual actions such as walking or typing style, speech or handwriting but also social interactions and communication. In other words, social communication is an indispensable part of our daily behavior. Therefore, a person's social connections, spatio-temporal information, style of interactions etc. Can be a good source of information to identify his social behavioral pattern. Based on this hypothesis, this paper introduces a novel kind of behavioral biometrics called Social Behavioral Biometrics (SBB) for the first time. The study includes identification of social behavioral biometric features from real and virtual domain and their prospective applications for the purpose of person authentication and verification.

Handbook Of Biometrics

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A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

Abstract: The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources—memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques “zonal thermal pattern analysis” and energy-efficient adaptive health monitoring system have been proposed towards green IoT.

Vision-based human activity recognition: a survey

Abstract: Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.