Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

Deep Learning for Motion Recognition

Automatic analysis and interpretation of human motion from visual data has been one of the most significant computer vision challenges since 1970. In recent years, deep learning has fueled the rapid advancement of computer vision topics. In particular, human motion analysis has drawn substantial attention due to its practical importance in many applications in a variety of domain including social behavior studies, medical assistance, robotics, sport analytics, and more. Human motion is one of the key parts of human social behavior and a rich source of information. We move our whole body involving head, shoulders, hands, trunk, legs, and limbs combined with facial expressions flavored with our individualized style to transmit social signals. A number of studies have suggested the existence of unique motion signatures of individuals by analyzing data obtained from KinectTM devices, and Electromyography (EMG) electrodes attached to muscles. Meaning that when we move and communicate, we tend to use our characteristic style of motion. These distinct motion patterns are attributed to behavioral and anatomical di↵erences between individuals as well as their di↵erent muscle activation strategies. This research aims at establishing a fully-automated framework to push the envelope of understanding information hidden in human motions from visual inputs and its potential applications on a set of fundamental tasks including classification, identification, and user authentication. For this purpose, we propose a number of deep learning approaches and try to tackle the problem from a data-driven perspective and figure out to what extend we would be able to model human motion signatures and see if it is possible to authenticate or identify people based on their movement pattern. Our results demonstrate an accuracy of 94.04% for human authentication and 92.62% for human identification among 10 subjects confirming that human motion conveys information regarding their identity and can be considered as practical biometric cues. Considering particular applications and their limitations, we further propose a generative biometric model that efficiently learns task-relevant features in data and integrate them into a probabilistic authentication setting based on limited amount of data. The proposed framework is able to authenticate the correct subject 86.11% of times

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Enriching mobile interaction with garment-based wearable computing devices

Wearable computing is on the brink of moving from research to mainstream. The first simple products, such as fitness wristbands and smart watches, hit the mass market and achieved considerable market penetration. However, the number and versatility of research prototypes in the field of wearable computing is far beyond the available devices on the market. Particularly, smart garments as a specific type of wearable computer, have high potential to change the way we interact with computing systems. Due to the proximity to the user`s body, smart garments allow to unobtrusively sense implicit and explicit user input. Smart garments are capable of sensing physiological information, detecting touch input, and recognizing the movement of the user. In this thesis, we explore how smart garments can enrich mobile interaction. Employing a user-centered design process, we demonstrate how different input and output modalities can enrich interaction capabilities of mobile devices such as mobile phones or smart watches. To understand the context of use, we chart the design space for mobile interaction through wearable devices. We focus on the device placement on the body as well as interaction modality. We use a probe-based research approach to systematically investigate the possible inputs and outputs for garment based wearable computing devices. We develop six different research probes showing how mobile interaction benefits from wearable computing devices and what requirements these devices pose for mobile operating systems. On the input side, we look at explicit input using touch and mid-air gestures as well as implicit input using physiological signals. Although touch input is well known from mobile devices, the limited screen real estate as well as the occlusion of the display by the input finger are challenges that can be overcome with touch-enabled garments. Additionally, mid-air gestures provide a more sophisticated and abstract form of input. We present a gesture elicitation study to address the special requirements of mobile interaction and present the resulting gesture set. As garments are worn, they allow different physiological signals to be sensed. We explore how we can leverage these physiological signals for implicit input. We conduct a study assessing physiological information by focusing on the workload of drivers in an automotive setting. We show that we can infer the driver´s workload using these physiological signals. Beside the input capabilities of garments, we explore how garments can be used as output. We present research probes covering the most important output modalities, namely visual, auditory, and haptic. We explore how low resolution displays can serve as a context display and how and where content should be placed on such a display. For auditory output, we investigate a novel authentication mechanism utilizing the closeness of wearable devices to the body. We show that by probing audio cues through the head of the user and re-recording them, user authentication is feasible. Last, we investigate EMS as a haptic feedback method. We show that by actuating the user`s body, an embodied form of haptic feedback can be achieved. From the aforementioned research probes, we distilled a set of design recommendations. These recommendations are grouped into interaction-based and technology-based recommendations and serve as a basis for designing novel ways of mobile interaction. We implement a system based on these recommendations. The system supports developers in integrating wearable sensors and actuators by providing an easy to use API for accessing these devices. In conclusion, this thesis broadens the understanding of how garment-based wearable computing devices can enrich mobile interaction. It outlines challenges and opportunities on an interaction and technological level. The unique characteristics of smart garments make them a promising technology for making the next step in mobile interaction

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll