Hand Gesture Recognition Using Particle Swarm Movement

We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gestures with assistive boundary indicators, and 94.2% for blind gesture spotting on digit gesture vocabulary. The proposed recognizer requires fewer computation resources; thus it is a good candidate for real-time applications

Inferences from Interactions with Smart Devices: Security Leaks and Defenses

We unlock our smart devices such as smartphone several times every day using a pin, password, or graphical pattern if the device is secured by one. The scope and usage of smart devices\u27 are expanding day by day in our everyday life and hence the need to make them more secure. In the near future, we may need to authenticate ourselves on emerging smart devices such as electronic doors, exercise equipment, power tools, medical devices, and smart TV remote control. While recent research focuses on developing new behavior-based methods to authenticate these smart devices, pin and password still remain primary methods to authenticate a user on a device. Although the recent research exposes the observation-based vulnerabilities, the popular belief is that the direct observation attacks can be thwarted by simple methods that obscure the attacker\u27s view of the input console (or screen). In this dissertation, we study the users\u27 hand movement pattern while they type on their smart devices. The study concentrates on the following two factors; (1) finding security leaks from the observed hand movement patterns (we showcase that the user\u27s hand movement on its own reveals the user\u27s sensitive information) and (2) developing methods to build lightweight, easy to use, and more secure authentication system. The users\u27 hand movement patterns were captured through video camcorder and inbuilt motion sensors such as gyroscope and accelerometer in the user\u27s device