The Application of Biometric Identification Technology in ATM System

2013 ◽  
Vol 347-350 ◽  
pp. 3419-3421
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Second Generation ◽  
Fingerprint Recognition ◽  
Biometric Identification ◽  
Automatic Identification ◽  
Behavioral Characteristics ◽  
Automatic Teller Machine ◽  
Biological Recognition ◽  
The Face ◽  
Bank System

Biometric identification technology deals with the identification of individuals based on their biological or human behavioral characteristics. Biometric identification method is reliable, anti-counterfeit, convenient and safe. At present there are some insecurity factors in the ATM (automatic teller machine) in bank system. Methods such as combining biological recognition with the ATM machine, adding the face recognition technology, fingerprint recognition, second generation ID card recognition, enhancing automatic identification are developed to improve the security of ATM.

scholarly journals Liveness Measurements Using Optical Flow for Biometric Person Authentication

2012 ◽  
Vol 19 (2) ◽  
pp. 257-268 ◽  
Author(s):  
Maciej Smiatacz
Keyword(s):  
Optical Flow ◽  
Video Sequences ◽  
Support Vector ◽  
Biometric Identification ◽  
Behavioral Characteristics ◽  
Security Level ◽  
Identification Algorithm ◽  
Face Region ◽  
The Face ◽  
Consumer Devices

Liveness Measurements Using Optical Flow for Biometric Person Authentication Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.

A Review on Biometric Technology

2018 ◽  
Vol 8 (4) ◽  
pp. 101
Author(s):  
Kartik Choudhary ◽  
Rizwan Khan
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Technology ◽  
The Face ◽  
The Individual ◽  
Recognition Technique ◽  
Better Than

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of   individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

The Research of Face Recognition Method Based on Wavelet Transform

2012 ◽  
Vol 442 ◽  
pp. 463-467
Author(s):  
Li Hong Bi ◽  
Yan Fang Ma ◽  
Li Hua Piao
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Biometric Identification ◽  
Recognition Method ◽  
Image Block ◽  
The Face ◽  
Speed And Accuracy ◽  
Great Development

Face recognition is a kind of biometric identification technology possessing great development potential, researching on this technology has great theoretical values. The paper presents a method of image block principal component analysis (PCA) based on wavelet transform. The image was firstly disposed by wavelet transform and segmented, then we set the different weight values for each of parts according to the different role in the overall image and extract eigenvector using the PCA, finally, the face image is recognized according to the eigenvector and feature. This method can improve the speed and accuracy, reduce the complexity of feature extraction and improve the speed of recognition.

scholarly journals Multimodal Identification Based on Fingerprint and Face Images via a Hetero-Associative Memory Method

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2976
Author(s):  
Qi Han ◽  
Heng Yang ◽  
Tengfei Weng ◽  
Guorong Chen ◽  
Jinyuan Liu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Asymptotic Stability ◽  
Associative Memory ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Face Images ◽  
The Face ◽  
Biometric Information ◽  
The Given ◽  
Biometric Features

Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.

A Study on Different Facial Features Extraction Technique

2016 ◽  
pp. 224-249
Author(s):  
Arnab Kumar Maji ◽  
Bandariakor Rymbai ◽  
Debdatta Kandar
Keyword(s):  
Face Recognition ◽  
Facial Feature ◽  
Biometric Identification ◽  
Facial Features ◽  
Human Face ◽  
Challenging Problem ◽  
Identity Verification ◽  
The Face ◽  
Changes Over Time ◽  
Natural Means

Facial recognition is the most natural means of biometric identification as it deals with the measurement of a biological relevance. Since, faces varies from each and every person, therefore, it can be used for security purpose. Face recognition is a very challenging problem, where the human face changes over time, as it depends on the pose, expression, occlusion, aging, etc. It can be used in many areas such as for surveillance purposes, security, general identity verification, criminal justice system, smart cards, etc. The most important part of the face recognition is the evaluation of facial features. With the help of facial feature, the system usually looks for the position of eyes, nose and mouth and distances between them can be detected and computed. This chapter will discuss some of the techniques that can be used to extract important facial features.

scholarly journals Fingerprint Authentication

2021 ◽  
Vol 10 (5) ◽  
pp. 87-89
Author(s):  
J Paul Rajasingh ◽  
D Sai Yaswanth
Keyword(s):  
Iris Recognition ◽  
Fingerprint Recognition ◽  
Automatic Identification ◽  
Biometric Recognition ◽  
Behavioural Characteristics ◽  
The Face ◽  
Signature Recognition ◽  
Personal Recognition ◽  
Vein Recognition ◽  
Hand Geometry

Biometrics refers to the automatic identification of a living person based on physiological or behavioural characteristics for authentication purpose. Among the existing biometric technologies are the face recognisation, fingerprint recognition, finger-geometry, hand geometry, iris recognition, vein recognition, voice recognition and signature recognition, Biometric method requires the physical presence of the person to be identified. This emphasizes its preference over the traditional method of identifying what you have such as, the use of password, a smartcard etc. Also, it potentially prevents unauthorized admittance to access control systems or fraudulent use of ATMs, Time Attendance Systems, cellular phones, smart cards, desktop PCs, Workstations, vehicles and computer networks. Biometric recognition systems offer greater security and convenience than traditional methods of personal recognition.

scholarly journals Feature Extraction Based Face Recognition Using Extreme Learning Machine (ELM)

2016 ◽  
pp. 80-83
Author(s):  
NAGABHAIRAVA VENKATA SIDDARTHA ◽  
MOHAMMAD UMAR ◽  
NABANKUR SEN ◽  
P. KRISHNA PRASAD
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extreme Learning Machine ◽  
Credit Card ◽  
Recognition System ◽  
Extraction Process ◽  
Biometric Identification ◽  
Face Recognition System ◽  
The Face ◽  
Learning Machine

In recent years, Face recognition becomes one of the popular biometric identification systems used in identifying or verifying individuals and matching it against library of known faces. Biometric identification is an actively growing area of research and used in electronic commerce, electronic banking, electronic passports, electronic licences and security applications. Face recognition finds its application in wide variety of areas like criminal identification, human - computer interaction, security systems, credit- card verification, teleconference, image and film processing. This paper suggests an automated face recognition system which extracts the features from the face. Feature extraction process includes locating the position of eyes, nostrils and mouth and determining the distances between those regions. From the extracted features, a database is created for known individuals. A virtual neural network is created based on Extreme Learning Machine (ELM).

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Underlying Mechanism ◽  
Inversion Effect ◽  
Configural Processing ◽  
Major Contributor ◽  
Face Inversion Effect ◽  
Configural Information ◽  
Face Inversion ◽  
The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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