Neural networks

A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008–2010. Research in iris biometrics has expanded so much that, although covering only 3 years and intentionally being selective about coverage, this new survey lists a larger number of references than the inception-through-2007 survey.

https://www.springer.com/cda/content/document/cda_downloaddocument/9781447144014-c1.pdf?SGWID=0-0-45-1366206-p174543831

A Survey of Iris Biometrics Research: 2008–2010

Long range iris recognition

Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.

Unimodal and Multimodal Biometric Sensing Systems: A Review

This study presents a novel approach to detect driver's drowsiness by applying two distinct methods in computer vision and image processing. The objective of this study is to combine both methods under one single profile instead of relied solely on a detection method to enhance the driver's drowsiness detection resolution. Therefore a non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state. In physiological part, photoplethysmography (PPG) is analysed for its changes in signals waveform from awake to drowsy state. Meanwhile, eyes pattern or motion in image processing is addressed to detect driver fatigue. Genetic algorithm with template-matching approach is designed to detect eye region and estimate the drowsiness in different metric standard based on eyes behaviour. Moreover, PPG drowsy signals are integrated with eyes motion to derive the final probability model for delivering valid and reliable drowsiness detection system. Indeed, the proposed system provides high competitive edge over existing arbitrary drowsiness detection system where the driver's health and mental states can be monitored in real-time without constraints.

Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection

This paper presents a new approach towards automobile safety and security. We propose three distinct but closely related concepts viz. an Iris Recognition system, a Drowsy Driver Detection system and a Distress Signalling system using non-intrusive machine vision based concepts. In recent time's automobile theft and fatigue related crashes have really magnified. In order to minimize these issues, we have incorporated Biometric security through Iris recognition that will help in authentication and an improved sleep detection and driver alert system by monitoring both the driver's eyes as well as senseing the heat variation of the body via infrared thermal sensor. Distress Signalling system is incorporated for drivers to get assistance from the Police in need without revealing it to people present around him. This paper combines computer vision, pattern recognition and optics. All image processing was performed using NI Vision Assistant. Also NI LabVIEW was used to take the current body temperature from the temperature sensors attached to the DAQ (Data Acquisition) Signal Accessory.

A Drowsy Driver Detection and security system

The control over electronic system using hand gestures is an innovative user interface that resolves the complications of using numerous remote controls for appliances. Founded on a unified set of hand gestures, this system interprets the user's hand gestures into pre-specified commands to control one or many devices simultaneously. However, of late, security has been of major concern among the people in using such a system, especially, at critical places like Home Entrances, Cashbox, etc. In order to minimize this issue, we have incorporated Biometric security through Iris recognition that will help in user authentication. Iris recognition, which is a relatively new biometric technology, has great advantages such as variability, stability and security. This proves to be very propitious for high security environments. This paper presents an elaboration of the methodologies employed including object recognition, artificial neural networks, and biometric security systems. All image processing was performed using NI Vision Assistant. In addition, NI LabVIEW was used to train and implement Neural Networks for Hand Gesture Classification. We have developed an automatic hand-gesture based control system that works after authentication (using Iris recognition) of the user.

A biometric security based electronic gadget control using hand gestures

This paper presents a hand gesture and a vision driven wheelchair system for physically handicapped people. The system is contrived to have a utilitarian design that caters to most of the disabilities rather than just one. In order to minimize system-human interaction, we have endeavored to present two distinct but closely related concepts viz. the hand gesture and vision based control system in conjunction for the wheelchair control. The wheelchair system is designed such that it provides for a natural and an intuitive user interface which can comprehend and react to in compliance with the users' instinctive volition which eases the mobility of the physically challenged people. All image processing was performed using NI Vision Assistant. NI LabVIEW was used to train and implement Neural Networks for Hand Gesture Classification and also to enable motion control. A prototype was developed on which all our experiments were successfully carried out. The system has been tested for users with varied hand shapes and proved to be extremely reliable.

A smart mobility solution for physically challenged

In recent times the security has become a major issue of concern among the people. There is multifold increase in the installation and deployment of security services around the world. The threat starts when an unwanted person tries to obtain access to any place or value. In such a scenario the correct identification of the person is necessary so as to restrict the unidentified people from gaining access. Biometric details of a person offer the possibility of identifying the person very accurately. Iris identification is one such technique. This paper presents two different approaches for iris identification viz. template matching and average value method. Both the methods were tested on variety of images from the available database [1].Template matching is done using NI Vision Assistant 7.1 and NI LabVIEW 7.1. MATLAB 7.4 was used to implement average value method.

Vikrant Gupta

Papers

A Drowsy Driver Detection and security system

A biometric security based electronic gadget control using hand gestures

A smart mobility solution for physically challenged

Efficient iris recognition method for identification