A contactless identification system based on hand shape features

This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies - nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms - have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications

Vascular Innovation Science

Vascular Innovation science are a standout amongst the most approaching innovations which is very secure. In this paperi introduces a study of machine learning techniques utilized for Vascular innovation development applications, and distinguishes significant exploration issues. Basic mission assets and applications require instruments to recognize when honest to goodness clients attempt to abuse their benefits; unquestionably biometrics serves to give such administrations

Biometric Verification System for Automated Teller Machine (ATM)

Biometric Verification System for Automated Teller Machine (ATM) will serve as an alternative for the current verification system that uses ATM card and personal identification number (PIN) to protect against fraud and effectively eliminating most common attempts to gain unauthorized access. With biometric technology, customer can gain access to their account through smart card approach combined with biometric technology to automatically identify individuals using their distinct physical or behavioral characteristics. The main objective of this project is to solve the problems that arise from using PIN as the base of ATM verification system. These include unauthorized access into financial accounts, stealing money, ATM fraud and many more. To ensure a reliable project output, the author had outlined the scope of study for the proposed project. It involves the study of ATM system, biometrics technology, architecture, the benefits and the drawback of each approach and the current trend in the market. The development of this system will be based on the RAD methodology

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

The Application of Hierarchical Clustering Algorithms for Recognition Using Biometrics of the Hand

In data analysis, the hierarchical clustering algorithms are powerful tools allowing to identify natural clusters, often without any priori information of the data structure, and are quite often used because provide a graphical representation of the resulting partitions, a hierarchy or dendrogram, revealing more information than non-hierarchical algorithms that returns a unique partition. Moreover, it is not necessary specify the number of clusters à priori. Cutting the dendrogram in different levels on the hierarchy produces different partitions and also, the use of different clusters aggregation methods for the same data set can produces different hierarchies and hence different partitions. So, several studies have been concerned with validate the resulting partitions comparing them, for instance, by the analysis of cohesion and separation of their clusters. The work presented here focuses on the problem of choosing the best partition in hierarchical clustering. The procedure to search for the best partition is made in the nested set of partitions, defined by the hierarchy. In traditional approaches each partition is defined by horizontal lines cutting the dendrogram at a determined level. In [3] is proposed an improved method, SEP/COP, to obtain the best partition, based on a wide set of partitions. In this paper we discuss these two types of approaches and we do a comparative study using a set of experiments using two-dimensional synthetic data sets and a real-world data set, based on the biometrics of the hands. This database is provided from Bosphorus Hand Database [36], in the context of recognition of the identity of a person by using the features of her hand/biometrics. We conclude that neither of the approaches proved consistently to perform better than the other, but the SEP/COP algorithm showed to be a better partition algorithm in situations like clusters with the approximately the same cardinality and well apart. Also, less depend of the used aggregation criteria and more robust to the presence of noise. Regarding to real data, the results of the experiments demonstrated that SEP/COP hierarchical clustering algorithms approach can contribute to identification systems based on the biometrics of the hands shape

Home Energy Security Prototype using Microcontroller Based on Fingerprint Sensor

The globalization era brings rapid development in technology.The human need for speed and easiness pushed them toinnovate, such as in the security field. Initially, the securitysystem was conducted manually and impractical compared tonowadays system. A security technology that is developed wasbiometric application, particularly fingerprint. Fingerprintbasedsecurity became a reliable enough system because of itsaccuracy level, safe, secure, and comfortable to be used ashousing security system identification. This research aimed todevelop a security system based on fingerprint biometric takenfrom previous researches by optimizing and upgrading theprevious weaknesses. This security system could be a solutionto a robbery that used Arduino UNO Atmega328P CH340 R3Board Micro USB port. The inputs were fingerprint sensor, 4x5keypad, and magnetic sensor, whereas the outputs were 12 Vsolenoid, 16x2 LCD, GSM SIM800L module, LED, andbuzzer. The advantage of this security system was its ability togive a danger sign in the form of noise when the systemdetected the wrong fingerprint or when it detects a forcedopening. The system would call the homeowner then. Otherthan that, this system notified the homeowner of all of theactivities through SMS so that it can be used as a long-distanceobservation. This system was completed with a push button toopen the door from the inside. The maximum fingerprints thatcould be stored were four users and one admin. The admin’sjob was to add/delete fingerprints, replace the home owner’sphone number, and change the system’s PIN. The resultsshowed that the fingerprint sensor read the prints in a relativelyfast time of 1.136 seconds. The average duration that wasneeded to send an SMS was 69 seconds while through call was3.2 seconds

A Review of the Fingerprint, Speaker Recognition, Face Recognition and Iris Recognition Based Biometric Identification Technologies

This paper reviews four biometric identification technologies (fingerprint, speaker recognition, face recognition and iris recognition). It discusses the mode of operation of each of the technologies and highlights their advantages and disadvantages