Biometric walk recognizer. Research and results on wearable sensor-based gait recognition

Gait is a biometric trait that can allow user authentication, though being classified as a "soft" one due to a certain lack in permanence, and to sensibility to specific conditions. The earliest research relies on computer vision-based approaches, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, which are able to capture the dynamics of the walking pattern through simpler 1D signals, has spurred a different research line. This capture modality can avoid some problems related to computer vision-based techniques, but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques, make this biometrics attractive and suggest to continue the investigations in this field. The first Chapters of this thesis deal with an introduction to biometrics, and more specifically to gait trait. A comprehensive review of technologies, approaches and strategies exploited by gait recognition proposals in the state-of-the-art is also provided. After such introduction, the contributions of this work are presented in details. Summarizing, it improves preceding result achieved during my Master Degree in Computer Science course of Biometrics and extended in my following Master Degree Thesis. The research deals with different strategies, including preprocessing and recognition techniques, applied to the gait biometrics, in order to allow both an automatic recognition and an improvement of the system accuracy

Mobiles and wearables: owner biometrics and authentication

We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)

Adapting Speech Recognition in Augmented Reality for Mobile Devices in Outdoor Environments

This paper describes the process of integrating automatic speech recognition (ASR) into a mobile application and explores the benefits and challenges of integrating speech with augmented reality (AR) in outdoor environments. The augmented reality allows end-users to interact with the information displayed and perform tasks, while increasing the user’s perception about the real world by adding virtual information to it. Speech is the most natural way of communication: it allows hands-free interaction and may allow end-users to quickly and easily access a range of features available. Speech recognition technology is often available in most of the current mobile devices, but it often uses Internet to receive the corresponding transcript from remote servers, e.g., Google speech recognition. However, in some outdoor environments, Internet is not always available or may be offered at poor quality. We integrated an off-line automatic speech recognition module into an AR application for outdoor usage that does not require Internet. Currently, speech interaction is used within the application to access five different features, namely: to take a photo, shoot a film, communicate, messaging related tasks, and to request information, either geographic, biometric, or climatic. The application makes available solutions to manage and interact with the mobile device, offering good usability. We have compared the online and off-line speech recognition systems in order to assess their adequacy to the tasks. Both systems were tested under different conditions, commonly found in outdoor environments, such as: Internet access quality, presence of noise, and distractions.info:eu-repo/semantics/publishedVersio

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games

Testing for Convolutional Neural Network-based Gait Authentication in Smartphones

Most online fraud involves identity thief, especially in financial services such as banking, commercial services, or home security. Passwords have always been one of the most reliable and common way to protect user identities. However, passwords can be guessed or breached. Biometric authentications have emerged to be a compliment way to improve the security. Nevertheless, biometric factors such as fingerprint or face recognition can also be spoofed. Additionally, those factors require either user interaction (touch to unlock) or additional hardware (surveillance camera). Therefore, the next level of security with lower risk of attack and less user friction is essentially needed. gait authentication is one of the viable solutions since gait is the signature of the way humans walk, and the analysis can be done passively without any user interactions. Several breakthroughs in terms of model accuracy and efficiency were reported across several state-of-the-art papers. For example, DeepSense reported the accuracy of 0.942±0.032 in Human Activity Recognition and 0.997±0.001 in User Identification. Although there have been research focusing on gait-analysis recently, there has not been a stan- dardized way to define proper testing workflow and techniques that are required to ensure the correctness and efficiency of gait application system, especially when it is done in production scale. This thesis will present a general workflow of Machine Learning (ML) system testing in gait au- thentication using V-model, as well as identifying the areas and components that requires testing, including data testing and performance testing in each ML-related components. This thesis will also suggest some adversarial cases that the model can fail to predict. Traditional testing technique such as differential testing will also be introduced as a testing candidate for gait segmentation. In addition, several metrics and testing ideas will also be suggested and experimented. At last, some interesting findings will be reported in the experimental results section, and some areas for further future work will also be mentioned

Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Real Time Person Tracking and Identification using the Kinect sensor

The objective of this project was to design and implement an automated tracking and user identification system for use in a smart home environment. Most existing smart home systems require users to either carry some sort of object that the house can identify or provide some sort of identification when they issue a command. Our project seeks to eliminate these inconveniences and enable users to issue commands to their smart home through simple, non-intrusive voice prompts. Microsoft¢s Kinect sensor unit was chosen as our design platform for its functionality and ease of use