Authentication (original) (raw)

The Implementation of Face Security for Authentication Implemented on Mobile Phone

2011

In this paper we are presenting the face recognition security for mobile phones. The model which has been applied for face recognition is Eigenface. The implementation consists from two parts: MATLAB and Droid Emulator for ANDROID mobile phones. The proposed implementation model has come as an idea, since today's mobile phones are computers in medium. We run our e-mails, agendas, storing data, using it for financial applications for viewing stock markets etc., and we would like to provide the approach of security model which will be based on face recognition as a biometric approach for authentication on mobile phones. Due the PIN vulnerability, as the most used mobile phone authentication mechanism we are presenting the approach which will enable a new level of mobile phone user's security. This has been tested with the database which consists from many images of facial expression. The algorithm which was implemented for mobile face recognition on MATLAB side is PCA. Limited with hardware capabilities we made of substitution between accuracy and computation complexity on the application. Proliferation of application and data has aim to increase the user need to protect the data which exist in mobile devices.

MOBILE BANKING USING ANDROID BASED BIOMETRICS SYSTEM

The objective of the proposed system is designed to perform Mobile Banking in a secured way. To achieve this, the concept of Biometric person recognition method is introduced through the mobile devices. Biometrics can be categorized into two categories, they are Physiological and Behavioral. Physiological is based upon the direct measurements of a part of the human body and Behavioral is based on the action performed by the user. So, the author decided to use the face recognition method, it is purely based on the physiological characteristic of the human body. The Face recognition technique implementation is done with the two existing algorithms (i.e) Viola Jones and Principle Component Analysis(PCA).The Viola Jones method is mainly used to check whether the image is present in the frame or not. The PCA method is based on Eigen values and Eigen vectors and performs distance calculation based on features of the face. With this proposed idea, secured access to restricted data/services during a standard Web Session is possible with the small handheld devices. For the image capturing and forwarding process the Android mobile is used

Face Recognition in Mobile Devices

International Journal of Computer Applications, 2013

In today's networked world,mobile phone playsvery important role, it affects all aspects of human daily life.The need to maintain the security of information in mobile device is becoming both increasingly important and increasinglydifficult. Some human features likefingerprints, face, hand geometry, voice and iris are used to provide an authentication for security systems to reach high security level instead of traditional password based systems. This paper presents a deployment of face recognition algorithms on mobile devices. Proposed approach uses PCA algorithm with FPIEand DCVon mobile device.In thispaper all calculations done on a mobile phone,in whicha small number of images were used for testing the system.System accuracy is 92% for appropriately chosen threshold, in which the time taken to recognize a face is approximately 0.35 sec and this can increase when database size increased.

Mobile Face Recognition Application using Eigen face Approaches for Android

Al-Mustansiriyah Journal of Science, 2019

Face recognition is one of current biometrics identification methods, that based on the measuring to one of human biological characteristics and utilize them to recognize individuals. these characteristics which are called biometric they are hard to fake because they identify a person by measuring one of its biological characteristics such as (finger print, iris print and face print). With the rapid improvement of mobile technologies that happen in last decade face recognition process can make using mobile phone, this paper explains the building of mobile face recognition system using Eigen face approach, Experimental results have been tested on a local data-set that has been created to analyze the efficiency of the application in various cases including different illumination conditions, variation of view, and orientation, the recognition rate of the application when testing on Galaxy Grand Prime + was 78.4. while The recognition rate when testing on Galaxy Note 5 was 82.4. The acc...

Face Recognition Using Eigen Faces Algorithm

Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this, demonstrates the face recognition system in android device using eigenfaces. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application.

IJERT-Biometric Face Recognition Payment System

International Journal of Engineering Research and Technology (IJERT), 2018

https://www.ijert.org/biometric-face-recognition-payment-system https://www.ijert.org/research/biometric-face-recognition-payment-system-IJERTCONV6IS13107.pdf Use of payment cards in various places such as shopping, restaurants, lodges and online payment for booking hotels, movie tickets, flight and train tickets etc are increasing day by day. So the problem is that a person has to carry payment cards along with him and keep the cards secure to use it all the time. This also lacked security. In the present work the biometric face recognition payments is used in all kinds of payments. Thus it avoids the need to memorize different passwords. Face recognition payment system is safe, secure and even easy to use. It is reliable and more efficient compared to other payment technologies. A general design of online payment system using face recognition is proposed. The methods adopted for face recognition are by finding the eigenfaces and Euclidean distance.

Key words- Face Detection, Eigenface, Feature

2010

In this paper a novel description of Face Detection-Recognition architecture through mobile devices is presented. Since the face detection/recognition is of importance in real-life scenarios, such as for authentication and security services and implementing it in mobile devices would provide a great value to society. This paper presents client-server architecture through the use of Bluetooth Technology through which face detection/recognition phases can be implemented. Key wordsFace Detection, Eigenface, Feature Extration

Face Recognition on Android Smartphones

International Journal of Intelligent Computing Research, 2014

Smartphones are becoming day by day more powerful. As the same time their use is becoming more and more common in applications such as transport, healthcare, security, and surveillance functioning as a multipurpose , ubiquitous device. In this paper we describe the preliminary results of our experience in using an Android smartphone as a tool which can be used to verify the identity of individuals through the use of two functionalities provided by the most recent generations of smartphones: NFC and face recognition. The scenario that we used for our tests was that of an emergency situation in which identities can be verified by comparing a picture stored in an id card with a picture taken on the spot.

Real-Time Face Recognition with Eigenface Method

International Journal of Image, Graphics and Signal Processing, 2019

Real-time face image recognition is a face recognition system that is done directly using a webcam camera from a computer. Face recognition system aims to implement a biometrics system as a real-time facial recognition system. This system is divided into two important processes, namely the training process and the identification process. The registration process is a process where a user registered their name in a system and then registers their face. Face data that has been registered will be used for the next process, namely the identification process. The face registration process uses face detection using the OpenCV library. The feature extraction process and introduction to the recognition system use the Eigenface method. The results of this study found that, the Eigenface method is able to detect faces accurately up to 4 people simultaneously. The greater the threshold value will result in a greater value of FRR, while there isn't any FAR value found from different thresholds. The level of lighting, poses, and facial distance from the camera when training and testing the face image heavily influences the use of the eigenface method.