This document presents a novel approach for generating non-invertible cryptographic keys from cancellable fingerprint templates. It first applies a one-way transformation to minutiae points extracted from fingerprints to generate cancellable templates. These templates are then used to generate cryptographic keys that are non-invertible, making it highly infeasible to derive the original fingerprint or templates from the key. The key generation process involves fingerprint preprocessing like normalization, segmentation, and minutiae extraction. False minutiae are removed before generating the cancellable template, which is then input to a cryptosystem to produce the non-recoverable key.
This document discusses research progress in mobile fingerprint template protection. It covers three main schemes: biometric key generation, fuzzy schemes, and non-invertible transforms. Biometric key generation aims to directly derive cryptographic keys from fingerprints to avoid storing biometric features or secret keys. Fuzzy schemes hide secrets within public information so keys can be retrieved through biometric matching. Non-invertible transforms store transformed biometric features instead of the original template. The document analyzes the advantages and limitations of different schemes and points out open issues for future research in mobile fingerprint template protection.
AN EVALUATION OF FINGERPRINT SECURITY USING NONINVERTIBLE BIOHASHIJNSA Journal
Biometric analysis for identifying verification is becoming a widespread reality. It is a very challenging and tedious task to develop a biometric template protection scheme which is anonymous, revocable and noninvertible while maintaining decent performance. Cancellable biometrics is one of the best methods used to resolve this problem. In this paper, a new method called as BioHashing which follows the technique of cancellable biometrics in the fingerprint domain is proposed. This proposed method does not require the re-alignment of fingerprints as all the minutiae are translated into a pre-defined two dimensional space based on a reference minutia. After that, the proposed Biohashing method is used to enforce the one-way property (non-invertibility) of the biometric template. The proposed approach is very much resistant to minor translation error and rotation distortion. An Equal Error Rates (EER) of less than 1% is achieved in this approach and performance of the approach is also significant.
This document summarizes a term paper on amalgamating biometrics and cryptography. It discusses the advantages of biometrics over passwords for authentication and introduces various biometric encryption techniques like key binding, key generation, fuzzy commitment, fuzzy vault, and cancelable biometrics. It reviews several approaches that apply these techniques to fingerprints, iris, signatures, and voice for secure key release and generation. The document outlines advantages of biometric cryptosystems and areas for future work to improve accuracy, security, and applications of biometric encryption.
MDD Project Report By Dharmendra singh [Srm University] Ncr DelhiDharmendrasingh417
In this modern era, a huge revolution in technology is the introduction of biometric recognition system. One of the most useful biometric recognition system is fingerprint recognition system. The fingerprint recognition system is considered to most important biometric system in addition to other biometrics recognition systems
A Novel Biometric Technique Benchmark Analysis For Selection Of Best Biometri...CSCJournals
A biometric security is a technique by means of which digital contents are protected by a cryptographic key generated from the biometric features of a person like Retina, Iris, Fingerprint, Face, Voice and so on. Normally the digital contents like documents are protected by a cryptographic key generated from a unique password. The process in irreversible, i.e the key can be generated from the password but not the vice versa. Passwords are relatively easy to hack as most of the users keep their personal information like date of birth as password and also password length has a limit as human beings cannot remember a password of significantly large length. Hence guessing the password of a user, whose significant information is available, is easier. Therefore off late lot of emphasis has been given to biometric features. Biometric features of no two people are same. For example the finger prints or the face of any two people differ. Hence if a template (alphanumeric or binary representation of features from a biometric data) is selected for the key generation than cracking them for accessing information becomes significantly difficult. But as with every advantage comes certain limitations also. The keys are not time invariant. Templates tends to change based on the data acquisition, or with time. For example the finger prints or palm prints changes with ages. Iris, retina and face features changes with change in light intensity during the acquisition phase. Fingerprint features changes with change in the orientation of the finger while scanning. In a classic authentication problem, such variability’s can be easily dealt with by keeping a threshold for the acceptance of the features. Such acceptance threshold is not applicable for the case of biometric templates. Even slightest of the variability in the templates changes the generated key, therefore causing a high false rejection rate. Hence in this work we analyze the most accepted biometric features and techniques for key generation and propose the most invariable technique in terms of data acquisition invariability. The work analyzes Iris, Face, Fingerprint and Palm prints for analysis of the biometric template generation and key generation form the templates. Further a unique benchmark analysis technique is proposed for quantifying the quality of a biometric model or features.
This document discusses enhancing biometric authentication for network security using iris recognition. It proposes using iris biometrics to generate secure authentication keys. The methodology involves preprocessing iris images, extracting minutiae feature points from the iris, generating a secret key from the minutiae, and using the key to encrypt and authenticate network access. Experimental results on two iris image datasets show the method effectively provides network security through iris-based encryption and authentication.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Overlapped Fingerprint Separation for Fingerprint AuthenticationIJERA Editor
Overlapped fingerprints captured at the crime scene plays significant role as an evidence to capture the criminals. As latent fingerprints are the accidently left skin impressions, so these are found to be with broken ridge composition, overlapped patterns and spoiled minutiae information. The Graphical User Interface (GUI) system is developed by using MATLAB R2015a software. This project also includes the development of standalone program for this system. The main purpose of GUI development is to get the value of real end points and real-branch points of a overlapped fingerprint image. The value of this point is used in fingerprint image matching process to identify the owner of an overlapped fingerprint image. The image enhancement consists of several process such as histogram equalization process, enhancement by Fast Fourier Transform (FFT) factor, and image binarization while minutiae extraction consist of ridge thinning process, region of interest (ROI) extraction, and minutiae extraction process. All processes should be done one by one.
This document discusses research progress in mobile fingerprint template protection. It covers three main schemes: biometric key generation, fuzzy schemes, and non-invertible transforms. Biometric key generation aims to directly derive cryptographic keys from fingerprints to avoid storing biometric features or secret keys. Fuzzy schemes hide secrets within public information so keys can be retrieved through biometric matching. Non-invertible transforms store transformed biometric features instead of the original template. The document analyzes the advantages and limitations of different schemes and points out open issues for future research in mobile fingerprint template protection.
AN EVALUATION OF FINGERPRINT SECURITY USING NONINVERTIBLE BIOHASHIJNSA Journal
Biometric analysis for identifying verification is becoming a widespread reality. It is a very challenging and tedious task to develop a biometric template protection scheme which is anonymous, revocable and noninvertible while maintaining decent performance. Cancellable biometrics is one of the best methods used to resolve this problem. In this paper, a new method called as BioHashing which follows the technique of cancellable biometrics in the fingerprint domain is proposed. This proposed method does not require the re-alignment of fingerprints as all the minutiae are translated into a pre-defined two dimensional space based on a reference minutia. After that, the proposed Biohashing method is used to enforce the one-way property (non-invertibility) of the biometric template. The proposed approach is very much resistant to minor translation error and rotation distortion. An Equal Error Rates (EER) of less than 1% is achieved in this approach and performance of the approach is also significant.
This document summarizes a term paper on amalgamating biometrics and cryptography. It discusses the advantages of biometrics over passwords for authentication and introduces various biometric encryption techniques like key binding, key generation, fuzzy commitment, fuzzy vault, and cancelable biometrics. It reviews several approaches that apply these techniques to fingerprints, iris, signatures, and voice for secure key release and generation. The document outlines advantages of biometric cryptosystems and areas for future work to improve accuracy, security, and applications of biometric encryption.
MDD Project Report By Dharmendra singh [Srm University] Ncr DelhiDharmendrasingh417
In this modern era, a huge revolution in technology is the introduction of biometric recognition system. One of the most useful biometric recognition system is fingerprint recognition system. The fingerprint recognition system is considered to most important biometric system in addition to other biometrics recognition systems
A Novel Biometric Technique Benchmark Analysis For Selection Of Best Biometri...CSCJournals
A biometric security is a technique by means of which digital contents are protected by a cryptographic key generated from the biometric features of a person like Retina, Iris, Fingerprint, Face, Voice and so on. Normally the digital contents like documents are protected by a cryptographic key generated from a unique password. The process in irreversible, i.e the key can be generated from the password but not the vice versa. Passwords are relatively easy to hack as most of the users keep their personal information like date of birth as password and also password length has a limit as human beings cannot remember a password of significantly large length. Hence guessing the password of a user, whose significant information is available, is easier. Therefore off late lot of emphasis has been given to biometric features. Biometric features of no two people are same. For example the finger prints or the face of any two people differ. Hence if a template (alphanumeric or binary representation of features from a biometric data) is selected for the key generation than cracking them for accessing information becomes significantly difficult. But as with every advantage comes certain limitations also. The keys are not time invariant. Templates tends to change based on the data acquisition, or with time. For example the finger prints or palm prints changes with ages. Iris, retina and face features changes with change in light intensity during the acquisition phase. Fingerprint features changes with change in the orientation of the finger while scanning. In a classic authentication problem, such variability’s can be easily dealt with by keeping a threshold for the acceptance of the features. Such acceptance threshold is not applicable for the case of biometric templates. Even slightest of the variability in the templates changes the generated key, therefore causing a high false rejection rate. Hence in this work we analyze the most accepted biometric features and techniques for key generation and propose the most invariable technique in terms of data acquisition invariability. The work analyzes Iris, Face, Fingerprint and Palm prints for analysis of the biometric template generation and key generation form the templates. Further a unique benchmark analysis technique is proposed for quantifying the quality of a biometric model or features.
This document discusses enhancing biometric authentication for network security using iris recognition. It proposes using iris biometrics to generate secure authentication keys. The methodology involves preprocessing iris images, extracting minutiae feature points from the iris, generating a secret key from the minutiae, and using the key to encrypt and authenticate network access. Experimental results on two iris image datasets show the method effectively provides network security through iris-based encryption and authentication.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Overlapped Fingerprint Separation for Fingerprint AuthenticationIJERA Editor
Overlapped fingerprints captured at the crime scene plays significant role as an evidence to capture the criminals. As latent fingerprints are the accidently left skin impressions, so these are found to be with broken ridge composition, overlapped patterns and spoiled minutiae information. The Graphical User Interface (GUI) system is developed by using MATLAB R2015a software. This project also includes the development of standalone program for this system. The main purpose of GUI development is to get the value of real end points and real-branch points of a overlapped fingerprint image. The value of this point is used in fingerprint image matching process to identify the owner of an overlapped fingerprint image. The image enhancement consists of several process such as histogram equalization process, enhancement by Fast Fourier Transform (FFT) factor, and image binarization while minutiae extraction consist of ridge thinning process, region of interest (ROI) extraction, and minutiae extraction process. All processes should be done one by one.
Biometric Fingerprint Recognintion based on Minutiae MatchingNabila mahjabin
The document summarizes a student's project on biometric fingerprint recognition based on minutiae matching. It includes an introduction to fingerprints and fingerprint recognition techniques. The project involves developing a complete fingerprint recognition system through minutiae extraction and matching. The system applies preprocessing techniques like image enhancement and binarization before extracting minutiae features from fingerprints. It then performs minutiae marking and false minutiae removal before matching fingerprints based on their minutiae patterns. The performance of the developed system is evaluated on a fingerprint database.
ADAPTABLE FINGERPRINT MINUTIAE EXTRACTION ALGORITHM BASED-ON CROSSING NUMBER ...IJCSEIT Journal
In this article, a main perspective of developing and implementing fingerprint extraction and matching
algorithms as a part of fingerprint recognition system is focused. First, developing a simple algorithm to
extract fingerprint features and test this algorithm on PC. The second thing is implementing this algorithm
into FPGA devices. The major research topics on which the proposed approach is developing and
modifying fingerprint extraction feature algorithm. This development and modification are using crossing
number method on pixel representation value ’0’. In this new proposed algorithm, it is no need a process
concerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parameters
using Angle Calculation Block avoiding floating points calculation. As this method is local feature that
usually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR, FRR and
EER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprint
minutiae extraction algorithm into hardware implementation with 14.05 % of EER, better than reference
algorithm, which is 20.39 % .The computational time is 18 seconds less than a similar method, which takes
60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardware
environment (embedded) using FPGA Device by developing IP Core without using any soft processor is
presented.
A Review on QR Code for Hiding Private InformationIRJET Journal
This document reviews research on using QR codes to hide private information. It summarizes several papers that propose techniques for embedding secret messages or facial biometrics in QR codes. The proposed system would generate a new two-level QR code with a public and private storage level for authentication purposes. The public level would contain readable information, while the private level would use patterns to encode encrypted data, increasing storage capacity and security over a standard QR code.
Review on Implementation Visual Cryptography & Steganography for Secure Authe...IRJET Journal
This document summarizes and reviews various techniques for implementing visual cryptography and steganography for secure authentication. It begins with an abstract that outlines using image processing and visual cryptography to divide a customer's transaction key into shares, with one share stored by the bank and one by the customer. The customer must present their share for transactions. The document then reviews the history of cyber attacks on major companies. It proposes a system using color image visual cryptography to encrypt passwords and divide them into shares between the bank and customer to authenticate transactions securely. It concludes that this approach could help solve issues with password hacking for core banking applications.
Enhancing Security of Multimodal Biometric Authentication System by Implement...IOSR Journals
Abstract : Conventional personal identification techniques for instance passwords, tokens, ID card and PIN
codes are prone to theft or forgery and thus biometrics isa solution thereto. Biometrics is the way of recognizing
and scrutinizing the physical traits of a person. Automated biometrics verification caters as a conducive and
legitimate method, but there must be an assurance to its cogency. Furthermore, in most of the cases unimodal
biometric recognition is not able to meet the performance requirements of the applications. According to recent
trends, recognition based on multimodal biometrics is emerging at a greater pace. Multimodal biometrics
unifies two or more biometric traits and thus the issues that emerge in unimodal recognition can be mitigated in
multimodal biometric systems. But with the rapid ontogenesis of information technology, even the biometric
data is not secure. Digital watermarking is one such technique that is implemented to secure the biometric data
from inadvertent or premeditated attacks.This paper propounds an approach that is projected in both the
directions of improving the performance of biometric identification system by going multimodal and, increasing
the security through watermarking. The biometric traits are initially transformed using Discrete Wavelet and
Discrete Cosine Transformation and then watermarked using Singular Value Decomposition. Scheme depiction
and presented outcomes justifies the effectiveness of the scheme.
Keywords: Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Multimodal biometrics,
Singular Value Decomposition, Watermarking
Biometrics Authentication of Fingerprint with Using Fingerprint Reader and Mi...TELKOMNIKA JOURNAL
The idea of security is as old as humanity itself. Between oldest methods of security were
included simple mechanical locks whose authentication element was the key. At first, a universal–simple
type, later unique for each lock. A long time had mechanical locks been the sole option for protection
against unauthorized access. The boom of biometrics has come in the 20th century, and especially in
recent years, biometrics is much expanded in the various areas of our life. Opposite of traditional security
methods such as passwords, access cards, and hardware keys, it offers many benefits. The main benefits
are the uniqueness and the impossibility of their loss. The main benefits are the uniqueness and the
impossibility of their loss. Therefore we focussed in this paper on the the design of low cost biometric
fingerprint system and subsequent implementation of this system in praxtise. Our main goal was to create
a system that is capable of recognizing fingerprints from a user and then processing them. The main part
of this system is the microcontroller Arduino Yun with an external interface to the scan of the fingerprint
with a name Adafruit R305 (special reader). This microcontroller communicates with the external database,
which ensures the exchange of data between Arduino Yun and user application. This application was
created for (currently) most widespread mobile operating system-Android.
This document summarizes a research paper that proposes a technique for secure authentication of bank customers using image processing, improved steganography, and visual cryptography. The technique encodes a customer's password using steganography to hide it in an image. The image is then divided into shares, with one share stored by the bank and one by the customer. During transactions, the customer presents their share which is combined with the bank's share to reconstruct the original image and extract the hidden password for authentication. The proposed method aims to improve imperceptibility compared to previous steganography methods by utilizing more surrounding pixels when hiding information to reduce image distortion.
Experimental study of minutiae based algorithm for fingerprint matchingcsandit
In this paper, a minutiae-based algorithm for fingerprint pattern recognition and matching is
proposed. The algorithm uses the distance between the minutiae and core points to determine
the pattern matching scores for fingerprint images. Experiments were conducted using
FVC2002 fingerprint database comprising four datasets of images of different sources and
qualities. False Match Rate (FMR), False Non-Match Rate (FNMR) and the Average Matching
Time (AMT) were the statistics generated for testing and measuring the performance of the
proposed algorithm. The comparative analysis of the proposed algorithm and some existing
minutiae based algorithms was carried out as well. The findings from the experimental study
were presented, interpreted and some conclusions were drawn.
Online Payment System using Steganography and Visual CryptographyIJCERT
In recent time there is rapid growth in E-Commerce market. Major concerns for customers in online shopping are debit card or credit card fraud and personal information security. Identity theft and phishing are common threats of online shopping. Phishing is a method of stealing personal confidential information such as username, passwords and credit card details from victims. It is a social engineering technique used to deceive users. In this paper new method is proposed that uses text based steganography and visual cryptography. It represents new approach which will provide limited information for fund transfer. This method secures the customer's data and increases customer's confidence and prevents identity theft.
ARTIFICIAL NEURAL CRYPTOGRAPHY DATAGRAM HIDING TECHNIQUES FOR COMPUTER SECURI...IAEME Publication
Cryptography is the scientific study of mathematical and algorithmic techniques relating to information security. Cryptographic techniques will help to protect information in cases where an attacker can have physical access to the bits representing the information, ex. When the information has to be sent over a communication channel that can be eaves dropped on by an attacker. Cryptographic primitives are the basic building blocks for constructing cryptographic solutions to information protection problems. A cryptographic primitive consists of one or more algorithms that achieve a number of protection goals. There is no well-agreed upon complete list of cryptographic primitives, nor are all cryptographic primitives independent, it is often possible to realize one primitive using a combination of other primitives.
Deep Unified Model for Intrusion Detection Based on Convolutional Neural NetworkYogeshIJTSRD
Indian army has always been subject to military attacks from neighbouring countries. Despite many surveillance devices and border security forces, the enemy finds a way to infiltrate deep into our borders. This is mainly because even now the surveillance in India is largely human assisted. Therefore this automated surveillance can authenticate the authorized persons and alert everyone when an enemy intrusion is detected. In this, we proposed an automated surveillance system that tackles the predicament of recognition of faces subject to different real time scenarios. This model incorporates a camera that captures the input image, an algorithm to detect a face from the input image, recognize the face using a convolution neural network along with transfer learning method, and verifies the detected person. The authorized person’s name and details are stored in CSV format and then into the database. In case of any unauthorized persons face is detected the image of the intruder along with time is stored in the database and warning signal is also given to alert the surrounding members in case of intrusion detection. Dhanu Shree D | Fouzia Fathima A | Madhumita B | Akila G | Thulasiram S "Deep Unified Model for Intrusion Detection Based on Convolutional Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39976.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39976/deep-unified-model-for-intrusion-detection-based-on-convolutional-neural-network/dhanu-shree-d
IRJET - Using Convolutional Neural Network in Surveillance Videos for Recogni...IRJET Journal
This document proposes a system to detect suspicious human activities in examination surveillance videos using a convolutional neural network model. It extracts optical flow features from video data and uses a 3D CNN for activity recognition. The system can recognize activities like object exchange, peering at answers, and person swapping. It detects these activities by recognizing faces, hands, and contact between hands and faces of different people. The model achieves better performance than other methods in detecting four abnormal behaviors from examination videos. It provides automated suspicious activity detection to help monitor examinations more accurately.
In wireless communications sensitive data is frequently changed, requiring remote authentication. Remote authentication involves the submission of encrypted data, along with visual and audio cues (facial images/videos, human voice etc.). Nonetheless, malicious program and different attacks will cause serious issues, particularly in cases of remote examinations or interviewing. This paper proposes a sturdy authentication mechanism supported semantic segmentation, chaotic cryptography and knowledge concealment. Assuming that user X needs to be remotely documented, initially X’s video object (VO) is mechanically segmental, employing a head and-body detector. Next, one amongst X’s biometric signals is encrypted by a chaotic cipher. Subsequently the encrypted signal is inserted to the most vital riffle coefficients of the VO, victimization its Qualified Significant riffle Trees (QSWTs). QSWTs give invisibility and vital resistance against loss transmission and compression, conditions that area unit typical in wireless networks. Finally, the Inverse distinct riffle rework (IDWT) is applied to supply the stegno-object (SO). Experimental results, regarding: (a) security deserves of the planned cryptography theme, (b) strength to stegno-analytic attacks, to numerous transmission losses and JPEG compression ratios and (c) information measure potency measures, indicate the promising performance of the planned biometrics-based authentication theme.
Biometrics Authentication Using Raspberry PiIJTET Journal
This document discusses a biometrics authentication system using fingerprint recognition on a Raspberry Pi. It uses a fingerprint reader module connected to a Raspberry Pi. Fingerprint images are captured using a GUI application and converted to binary templates. The templates are stored in a PostgreSQL database. A Python script is used to match fingerprints by comparing templates and identifying matching ridge patterns between fingerprints. The system was able to accurately match fingerprints from the same finger and distinguish fingerprints from different fingers based on the ridge patterns. Future work involves improving the matching accuracy and developing the system for real-time high-end applications.
Authentication Schemes for Session Passwords using Color and ImagesIJNSA Journal
Textual passwords are the most common method used for authentication. But textual passwords are vulnerable to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are vulnerable to shoulder surfing. To address this problem, text can be combined with images or colors to generate session passwords for authentication. Session passwords can be used only once and every time a new password is generated. In this paper, two techniques are proposed to generate session passwords using text and colors which are resistant to shoulder surfing. These methods are suitable for Personal Digital Assistants.
A novel fast-chaff-point-generation-method-using-bioinspired-flower-pollinati...Karthikeyan Ece venkatesan
This document summarizes a research paper that proposes a new fast method for generating chaff points using a bio-inspired flower pollination algorithm for fuzzy vault systems used in wireless body area sensor networks. Fuzzy vaults are used to securely store a cryptographic key by binding it to biometric data, like physiological signals. Existing chaff point generation methods are computationally expensive. The proposed method uses a flower pollination algorithm to generate chaff points much faster, in just 0.49 milliseconds. It provides a concise yet high-level overview of the background, related work, and proposed fast chaff point generation method using bio-inspired algorithms.
COST-EFFECTIVE AUTHENTIC AND ANONYMOUS DATA SHARING WITH FORWARD SECURITYNexgen Technology
This document discusses cost-effective and anonymous data sharing using forward secure identity-based ring signatures. It proposes a new notion of forward secure ID-based ring signatures that allow ID-based ring signature schemes to provide forward security. This is the first scheme to provide this feature for ring signatures in an ID-based setting. The scheme provides unconditional anonymity and can be proven to be forward-securely unforgeable in the random oracle model under the RSA assumption. It is efficient, requiring only one exponentiation for key updates and no pairings. This scheme enables authentic and anonymous data sharing in large-scale systems like smart grids.
Diploma Project titles 2013 for CSE,IT,EEE,ECE/ B.Sc, BCA Project Titles.pdfIrissolution
Iris Solutions is a Leading R&D CompanyWe Providing Final Year Projects & Courses with Innovative training Methods..All classes Handling By Well Qualified Staffs. Also We Having Very-good Infrastructure.Job support for qualified candidates. Projects in Java, J2ee, Vb, C#, .Net, Embedded, VLSI & Matlab. domain Using Networking, Network security, Mobile computing, Image Processing,etc......Eligibility:M.E /M.TECH, MCA, M.Sc(CSE, IT)B.E/ B.TECH (ECE, EEE, E&I, ICE, CSE, IT)DIPLOMA (ECE, E&I, EEE, CSE, IT, ROBOTICS)BCA, B.Sc (CSE, IT)
Iris Solutions is a Leading R&D CompanyWe Providing Final Year Projects & Courses with Innovative training Methods..All classes Handling By Well Qualified Staffs. Also We Having Very-good Infrastructure.Job support for qualified candidates. Projects in Java, J2ee, Vb, C#, .Net, Embedded, VLSI & Matlab. domain Using Networking, Network security, Mobile computing, Image Processing,etc......Eligibility:M.E /M.TECH, MCA, M.Sc(CSE, IT)B.E/ B.TECH (ECE, EEE, E&I, ICE, CSE, IT)DIPLOMA (ECE, E&I, EEE, CSE, IT, ROBOTICS)BCA, B.Sc (CSE, IT)
IRJET- Secure Vault System using Iris Biometrics and PIC MicrocontrollerIRJET Journal
This document describes a secure vault system using iris biometrics and a PIC microcontroller for authentication. The system works by capturing iris images, segmenting the iris region, extracting features from the iris, and matching features to stored templates to authenticate users. When a match is found, the locker number is sent via RF transmitter to a robot, which then opens the corresponding locker. The system aims to provide a more secure and convenient alternative to traditional locker systems.
Biometric Template Protection With Robust Semi – Blind Watermarking Using Ima...CSCJournals
This paper addresses a biometric watermarking technology sturdy towards image manipulations, like JPEG compression, image filtering, and additive noise. Application scenarios include information transmission between client and server, maintaining e-database and management of signatures through insecure distribution channels. Steps involved in this work are, a) generation of binary signature code for biometric, b) embedding of the binary signature to the host image using intrinsic local property, that ensures signature protection, c) host image is then made exposed to various attacks and d) signature is extracted and matched based on an empirical threshold to verify the robustness of proposed embedding method. Embedding relies on binary signature manipulating the lower order AC coefficients of Discrete Cosine Transformed sub-blocks of host image. In the prediction phase, DC values of the nearest neighbor DCT blocks is utilized to predict the AC coefficients of centre block. Surrounding DC values of a DCT blocks are adaptively weighed for AC coefficients prediction. Linear programming is used to calculate the weights with respect to the image content. Multiple times embedding of watermark ensures robustness against common signal processing operations (filtering, enhancement, rescaling etc.) and various attacks. The proposed algorithm is tested for 50 different types of host images and public data collection, DB3, FVC2002. FAR and FRR are compared with other methods to show the improvement.
It seems like you're providing information about the publication process of the International Journal of Advanced Publication Practices. This information outlines the fast publication schedule and peer-review process by the journal of the appears to prioritize a fast and efficient publication process while maintaining the quality and integrity of the research it publishes of the best pharma journals .
Biometric Fingerprint Recognintion based on Minutiae MatchingNabila mahjabin
The document summarizes a student's project on biometric fingerprint recognition based on minutiae matching. It includes an introduction to fingerprints and fingerprint recognition techniques. The project involves developing a complete fingerprint recognition system through minutiae extraction and matching. The system applies preprocessing techniques like image enhancement and binarization before extracting minutiae features from fingerprints. It then performs minutiae marking and false minutiae removal before matching fingerprints based on their minutiae patterns. The performance of the developed system is evaluated on a fingerprint database.
ADAPTABLE FINGERPRINT MINUTIAE EXTRACTION ALGORITHM BASED-ON CROSSING NUMBER ...IJCSEIT Journal
In this article, a main perspective of developing and implementing fingerprint extraction and matching
algorithms as a part of fingerprint recognition system is focused. First, developing a simple algorithm to
extract fingerprint features and test this algorithm on PC. The second thing is implementing this algorithm
into FPGA devices. The major research topics on which the proposed approach is developing and
modifying fingerprint extraction feature algorithm. This development and modification are using crossing
number method on pixel representation value ’0’. In this new proposed algorithm, it is no need a process
concerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parameters
using Angle Calculation Block avoiding floating points calculation. As this method is local feature that
usually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR, FRR and
EER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprint
minutiae extraction algorithm into hardware implementation with 14.05 % of EER, better than reference
algorithm, which is 20.39 % .The computational time is 18 seconds less than a similar method, which takes
60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardware
environment (embedded) using FPGA Device by developing IP Core without using any soft processor is
presented.
A Review on QR Code for Hiding Private InformationIRJET Journal
This document reviews research on using QR codes to hide private information. It summarizes several papers that propose techniques for embedding secret messages or facial biometrics in QR codes. The proposed system would generate a new two-level QR code with a public and private storage level for authentication purposes. The public level would contain readable information, while the private level would use patterns to encode encrypted data, increasing storage capacity and security over a standard QR code.
Review on Implementation Visual Cryptography & Steganography for Secure Authe...IRJET Journal
This document summarizes and reviews various techniques for implementing visual cryptography and steganography for secure authentication. It begins with an abstract that outlines using image processing and visual cryptography to divide a customer's transaction key into shares, with one share stored by the bank and one by the customer. The customer must present their share for transactions. The document then reviews the history of cyber attacks on major companies. It proposes a system using color image visual cryptography to encrypt passwords and divide them into shares between the bank and customer to authenticate transactions securely. It concludes that this approach could help solve issues with password hacking for core banking applications.
Enhancing Security of Multimodal Biometric Authentication System by Implement...IOSR Journals
Abstract : Conventional personal identification techniques for instance passwords, tokens, ID card and PIN
codes are prone to theft or forgery and thus biometrics isa solution thereto. Biometrics is the way of recognizing
and scrutinizing the physical traits of a person. Automated biometrics verification caters as a conducive and
legitimate method, but there must be an assurance to its cogency. Furthermore, in most of the cases unimodal
biometric recognition is not able to meet the performance requirements of the applications. According to recent
trends, recognition based on multimodal biometrics is emerging at a greater pace. Multimodal biometrics
unifies two or more biometric traits and thus the issues that emerge in unimodal recognition can be mitigated in
multimodal biometric systems. But with the rapid ontogenesis of information technology, even the biometric
data is not secure. Digital watermarking is one such technique that is implemented to secure the biometric data
from inadvertent or premeditated attacks.This paper propounds an approach that is projected in both the
directions of improving the performance of biometric identification system by going multimodal and, increasing
the security through watermarking. The biometric traits are initially transformed using Discrete Wavelet and
Discrete Cosine Transformation and then watermarked using Singular Value Decomposition. Scheme depiction
and presented outcomes justifies the effectiveness of the scheme.
Keywords: Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Multimodal biometrics,
Singular Value Decomposition, Watermarking
Biometrics Authentication of Fingerprint with Using Fingerprint Reader and Mi...TELKOMNIKA JOURNAL
The idea of security is as old as humanity itself. Between oldest methods of security were
included simple mechanical locks whose authentication element was the key. At first, a universal–simple
type, later unique for each lock. A long time had mechanical locks been the sole option for protection
against unauthorized access. The boom of biometrics has come in the 20th century, and especially in
recent years, biometrics is much expanded in the various areas of our life. Opposite of traditional security
methods such as passwords, access cards, and hardware keys, it offers many benefits. The main benefits
are the uniqueness and the impossibility of their loss. The main benefits are the uniqueness and the
impossibility of their loss. Therefore we focussed in this paper on the the design of low cost biometric
fingerprint system and subsequent implementation of this system in praxtise. Our main goal was to create
a system that is capable of recognizing fingerprints from a user and then processing them. The main part
of this system is the microcontroller Arduino Yun with an external interface to the scan of the fingerprint
with a name Adafruit R305 (special reader). This microcontroller communicates with the external database,
which ensures the exchange of data between Arduino Yun and user application. This application was
created for (currently) most widespread mobile operating system-Android.
This document summarizes a research paper that proposes a technique for secure authentication of bank customers using image processing, improved steganography, and visual cryptography. The technique encodes a customer's password using steganography to hide it in an image. The image is then divided into shares, with one share stored by the bank and one by the customer. During transactions, the customer presents their share which is combined with the bank's share to reconstruct the original image and extract the hidden password for authentication. The proposed method aims to improve imperceptibility compared to previous steganography methods by utilizing more surrounding pixels when hiding information to reduce image distortion.
Experimental study of minutiae based algorithm for fingerprint matchingcsandit
In this paper, a minutiae-based algorithm for fingerprint pattern recognition and matching is
proposed. The algorithm uses the distance between the minutiae and core points to determine
the pattern matching scores for fingerprint images. Experiments were conducted using
FVC2002 fingerprint database comprising four datasets of images of different sources and
qualities. False Match Rate (FMR), False Non-Match Rate (FNMR) and the Average Matching
Time (AMT) were the statistics generated for testing and measuring the performance of the
proposed algorithm. The comparative analysis of the proposed algorithm and some existing
minutiae based algorithms was carried out as well. The findings from the experimental study
were presented, interpreted and some conclusions were drawn.
Online Payment System using Steganography and Visual CryptographyIJCERT
In recent time there is rapid growth in E-Commerce market. Major concerns for customers in online shopping are debit card or credit card fraud and personal information security. Identity theft and phishing are common threats of online shopping. Phishing is a method of stealing personal confidential information such as username, passwords and credit card details from victims. It is a social engineering technique used to deceive users. In this paper new method is proposed that uses text based steganography and visual cryptography. It represents new approach which will provide limited information for fund transfer. This method secures the customer's data and increases customer's confidence and prevents identity theft.
ARTIFICIAL NEURAL CRYPTOGRAPHY DATAGRAM HIDING TECHNIQUES FOR COMPUTER SECURI...IAEME Publication
Cryptography is the scientific study of mathematical and algorithmic techniques relating to information security. Cryptographic techniques will help to protect information in cases where an attacker can have physical access to the bits representing the information, ex. When the information has to be sent over a communication channel that can be eaves dropped on by an attacker. Cryptographic primitives are the basic building blocks for constructing cryptographic solutions to information protection problems. A cryptographic primitive consists of one or more algorithms that achieve a number of protection goals. There is no well-agreed upon complete list of cryptographic primitives, nor are all cryptographic primitives independent, it is often possible to realize one primitive using a combination of other primitives.
Deep Unified Model for Intrusion Detection Based on Convolutional Neural NetworkYogeshIJTSRD
Indian army has always been subject to military attacks from neighbouring countries. Despite many surveillance devices and border security forces, the enemy finds a way to infiltrate deep into our borders. This is mainly because even now the surveillance in India is largely human assisted. Therefore this automated surveillance can authenticate the authorized persons and alert everyone when an enemy intrusion is detected. In this, we proposed an automated surveillance system that tackles the predicament of recognition of faces subject to different real time scenarios. This model incorporates a camera that captures the input image, an algorithm to detect a face from the input image, recognize the face using a convolution neural network along with transfer learning method, and verifies the detected person. The authorized person’s name and details are stored in CSV format and then into the database. In case of any unauthorized persons face is detected the image of the intruder along with time is stored in the database and warning signal is also given to alert the surrounding members in case of intrusion detection. Dhanu Shree D | Fouzia Fathima A | Madhumita B | Akila G | Thulasiram S "Deep Unified Model for Intrusion Detection Based on Convolutional Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39976.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39976/deep-unified-model-for-intrusion-detection-based-on-convolutional-neural-network/dhanu-shree-d
IRJET - Using Convolutional Neural Network in Surveillance Videos for Recogni...IRJET Journal
This document proposes a system to detect suspicious human activities in examination surveillance videos using a convolutional neural network model. It extracts optical flow features from video data and uses a 3D CNN for activity recognition. The system can recognize activities like object exchange, peering at answers, and person swapping. It detects these activities by recognizing faces, hands, and contact between hands and faces of different people. The model achieves better performance than other methods in detecting four abnormal behaviors from examination videos. It provides automated suspicious activity detection to help monitor examinations more accurately.
In wireless communications sensitive data is frequently changed, requiring remote authentication. Remote authentication involves the submission of encrypted data, along with visual and audio cues (facial images/videos, human voice etc.). Nonetheless, malicious program and different attacks will cause serious issues, particularly in cases of remote examinations or interviewing. This paper proposes a sturdy authentication mechanism supported semantic segmentation, chaotic cryptography and knowledge concealment. Assuming that user X needs to be remotely documented, initially X’s video object (VO) is mechanically segmental, employing a head and-body detector. Next, one amongst X’s biometric signals is encrypted by a chaotic cipher. Subsequently the encrypted signal is inserted to the most vital riffle coefficients of the VO, victimization its Qualified Significant riffle Trees (QSWTs). QSWTs give invisibility and vital resistance against loss transmission and compression, conditions that area unit typical in wireless networks. Finally, the Inverse distinct riffle rework (IDWT) is applied to supply the stegno-object (SO). Experimental results, regarding: (a) security deserves of the planned cryptography theme, (b) strength to stegno-analytic attacks, to numerous transmission losses and JPEG compression ratios and (c) information measure potency measures, indicate the promising performance of the planned biometrics-based authentication theme.
Biometrics Authentication Using Raspberry PiIJTET Journal
This document discusses a biometrics authentication system using fingerprint recognition on a Raspberry Pi. It uses a fingerprint reader module connected to a Raspberry Pi. Fingerprint images are captured using a GUI application and converted to binary templates. The templates are stored in a PostgreSQL database. A Python script is used to match fingerprints by comparing templates and identifying matching ridge patterns between fingerprints. The system was able to accurately match fingerprints from the same finger and distinguish fingerprints from different fingers based on the ridge patterns. Future work involves improving the matching accuracy and developing the system for real-time high-end applications.
Authentication Schemes for Session Passwords using Color and ImagesIJNSA Journal
Textual passwords are the most common method used for authentication. But textual passwords are vulnerable to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are vulnerable to shoulder surfing. To address this problem, text can be combined with images or colors to generate session passwords for authentication. Session passwords can be used only once and every time a new password is generated. In this paper, two techniques are proposed to generate session passwords using text and colors which are resistant to shoulder surfing. These methods are suitable for Personal Digital Assistants.
A novel fast-chaff-point-generation-method-using-bioinspired-flower-pollinati...Karthikeyan Ece venkatesan
This document summarizes a research paper that proposes a new fast method for generating chaff points using a bio-inspired flower pollination algorithm for fuzzy vault systems used in wireless body area sensor networks. Fuzzy vaults are used to securely store a cryptographic key by binding it to biometric data, like physiological signals. Existing chaff point generation methods are computationally expensive. The proposed method uses a flower pollination algorithm to generate chaff points much faster, in just 0.49 milliseconds. It provides a concise yet high-level overview of the background, related work, and proposed fast chaff point generation method using bio-inspired algorithms.
COST-EFFECTIVE AUTHENTIC AND ANONYMOUS DATA SHARING WITH FORWARD SECURITYNexgen Technology
This document discusses cost-effective and anonymous data sharing using forward secure identity-based ring signatures. It proposes a new notion of forward secure ID-based ring signatures that allow ID-based ring signature schemes to provide forward security. This is the first scheme to provide this feature for ring signatures in an ID-based setting. The scheme provides unconditional anonymity and can be proven to be forward-securely unforgeable in the random oracle model under the RSA assumption. It is efficient, requiring only one exponentiation for key updates and no pairings. This scheme enables authentic and anonymous data sharing in large-scale systems like smart grids.
Diploma Project titles 2013 for CSE,IT,EEE,ECE/ B.Sc, BCA Project Titles.pdfIrissolution
Iris Solutions is a Leading R&D CompanyWe Providing Final Year Projects & Courses with Innovative training Methods..All classes Handling By Well Qualified Staffs. Also We Having Very-good Infrastructure.Job support for qualified candidates. Projects in Java, J2ee, Vb, C#, .Net, Embedded, VLSI & Matlab. domain Using Networking, Network security, Mobile computing, Image Processing,etc......Eligibility:M.E /M.TECH, MCA, M.Sc(CSE, IT)B.E/ B.TECH (ECE, EEE, E&I, ICE, CSE, IT)DIPLOMA (ECE, E&I, EEE, CSE, IT, ROBOTICS)BCA, B.Sc (CSE, IT)
Iris Solutions is a Leading R&D CompanyWe Providing Final Year Projects & Courses with Innovative training Methods..All classes Handling By Well Qualified Staffs. Also We Having Very-good Infrastructure.Job support for qualified candidates. Projects in Java, J2ee, Vb, C#, .Net, Embedded, VLSI & Matlab. domain Using Networking, Network security, Mobile computing, Image Processing,etc......Eligibility:M.E /M.TECH, MCA, M.Sc(CSE, IT)B.E/ B.TECH (ECE, EEE, E&I, ICE, CSE, IT)DIPLOMA (ECE, E&I, EEE, CSE, IT, ROBOTICS)BCA, B.Sc (CSE, IT)
IRJET- Secure Vault System using Iris Biometrics and PIC MicrocontrollerIRJET Journal
This document describes a secure vault system using iris biometrics and a PIC microcontroller for authentication. The system works by capturing iris images, segmenting the iris region, extracting features from the iris, and matching features to stored templates to authenticate users. When a match is found, the locker number is sent via RF transmitter to a robot, which then opens the corresponding locker. The system aims to provide a more secure and convenient alternative to traditional locker systems.
Biometric Template Protection With Robust Semi – Blind Watermarking Using Ima...CSCJournals
This paper addresses a biometric watermarking technology sturdy towards image manipulations, like JPEG compression, image filtering, and additive noise. Application scenarios include information transmission between client and server, maintaining e-database and management of signatures through insecure distribution channels. Steps involved in this work are, a) generation of binary signature code for biometric, b) embedding of the binary signature to the host image using intrinsic local property, that ensures signature protection, c) host image is then made exposed to various attacks and d) signature is extracted and matched based on an empirical threshold to verify the robustness of proposed embedding method. Embedding relies on binary signature manipulating the lower order AC coefficients of Discrete Cosine Transformed sub-blocks of host image. In the prediction phase, DC values of the nearest neighbor DCT blocks is utilized to predict the AC coefficients of centre block. Surrounding DC values of a DCT blocks are adaptively weighed for AC coefficients prediction. Linear programming is used to calculate the weights with respect to the image content. Multiple times embedding of watermark ensures robustness against common signal processing operations (filtering, enhancement, rescaling etc.) and various attacks. The proposed algorithm is tested for 50 different types of host images and public data collection, DB3, FVC2002. FAR and FRR are compared with other methods to show the improvement.
It seems like you're providing information about the publication process of the International Journal of Advanced Publication Practices. This information outlines the fast publication schedule and peer-review process by the journal of the appears to prioritize a fast and efficient publication process while maintaining the quality and integrity of the research it publishes of the best pharma journals .
IRJET- A Noval and Efficient Revolving Flywheel Pin Entry Method Resilient to...IRJET Journal
The document proposes a new authentication method called the revolving flywheel PIN-entry method to prevent shoulder surfing attacks. The method uses a revolving flywheel with three layers and sections containing randomly placed numbers and colors. Users register a PIN and for authentication must enter the PIN by clicking color pads associated with the numbers on the flywheel instead of entering the actual digits. The method aims to provide secure, usable authentication in a short time period and could be applied to systems like ATMs.
Enhancing the Cash Point using Multimode Biometric Systemijtsrd
Frauds attacking the automated teller machine have increased over the decade which has motivated us to use the biometrics for personal identification to procure high level of security and accuracy This paper describes a system that replaces the ATM cards and PINs by the physiological biometric face and iris authentication. In this system during enrolment the genuine users face and iris samples of are retained in the database. The process of transaction begins by getting and matching face and iris patterns. The system will automatically distinguish between real legitimate trait and fake samples. If a fake biometric is recognized a GSM module connected to the controller will message OTP by the system to the registered mobile number. After the valid OTP is entered the user can either withdraw or deposit cash or check his her balance. We have included another biometric called Palm Vien. By using Palm Vein we can access the ATM. Mr. T. Karthikeyan | Ms. Aarthi. S | Ms. Amirtha. P | Ms. Divya. R | Ms. Sowndarya. S ""Enhancing the Cash Point using Multimode Biometric System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21768.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/21768/enhancing-the-cash-point-using-multimode-biometric-system/mr-t-karthikeyan
The document describes a proposed 3D password authentication scheme. The scheme would present users with a 3D virtual environment containing various objects that they could interact with. A user's 3D password would be the specific sequence of interactions with different objects in the environment, such as typing on a virtual keyboard, providing fingerprint authentication at a device, or selecting radio channels in a virtual car. The scheme aims to combine elements of textual passwords, graphical passwords, biometrics, and tokens into a single 3D environment. Designing the virtual environment and selecting distinct object types and locations would determine the size of the possible password space. The scheme is presented as an alternative to traditional authentication methods that aims to be more secure, usable and flexible.
Advance Intelligent Video Surveillance System Using OpenCVIRJET Journal
This document describes the development of an intelligent video surveillance system using OpenCV. The proposed system aims to reduce electricity usage and storage needs by only recording video when human presence is detected, as opposed to continuous recording. It utilizes a camera initialized through OpenCV to capture video frames. The frames are converted to grayscale and analyzed using a Haar cascade classifier to detect human faces. If a face is detected, recording begins. If no motion is detected for several seconds, recording will stop. The recorded videos are stored locally. This approach is well-suited for locations with intermittent human presence, where continuous recording is unnecessary. It allows for more efficient use of resources than traditional CCTV.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Safety Helmet Detection in Engineering and ManagementIRJET Journal
This document describes a study that developed a deep learning-based model using the YOLO-V3 algorithm to detect whether workers on a construction site are wearing safety helmets in real-time. Video from the construction site is analyzed by the model, which can identify people and determine if they have a helmet on. If a worker is identified without a helmet, an alert is sent via SMS and buzzer to notify the worker and manager. The model achieved 92% accuracy in helmet detection testing, improving on previous methods. The goal is to help prevent accidents and injuries by monitoring helmet usage and providing alerts when safety protocols are not followed.
A survey paper on various biometric security system methodsIRJET Journal
This document summarizes various biometric security systems for identification. It discusses fingerprint recognition, iris recognition, and face recognition methods. It provides an overview of different approaches that have been proposed, including using watermarking, edge detection techniques, adaptive boosting algorithms, and fuzzy logic. The document also analyzes the drawbacks of previous methods and proposes using a multimodal biometric system that fuses fingerprints, iris, and face for more secure identification. Overall, the document surveys different biometric identification techniques and highlights that a multimodal approach can help overcome limitations of individual methods.
This document presents a proposed methodology for offline signature recognition. It begins with an introduction to biometrics and signature recognition. It then defines the problem of determining whose signature an image belongs to. The proposed methodology includes image acquisition, pre-processing steps like conversion to grayscale and thinning, feature extraction of global and grid features, training a neural network, and testing. It concludes that combining global and grid features extracted using discrete wavelet transform achieves recognition accuracy rates ranging from 93-89% for databases of 10 to 50 signatures.
Design and development of dorsal hand vein recognition biometric system usin...Raghavendra DC
The document presents a proposal for developing a dorsal hand vein recognition biometric system using image processing on an FPGA. The system would use near-infrared imaging to capture images of hand veins, extract features like vein patterns using thinning, and match patterns to authenticate users, signaling authentication with an LED. The methodology would involve preprocessing, segmentation, feature extraction and matching algorithms implemented on an FPGA using Verilog with a camera and LED interface. The system is intended to provide high security through unique and difficult to forge vein patterns.
The document describes a face recognition system that uses OpenCV to identify known and unknown faces from images and videos in real-time. The system detects faces, extracts facial features, compares the features to a database of known faces to recognize individuals, and stores details of unknown faces for attendance tracking purposes. It aims to automate attendance management more efficiently than manual methods. The system achieves high accuracy around 85% and could improve security and convenience for applications like online education, virtual meetings, and residential security.
Portable and Efficient Fingerprint Authentication System Based on a Microcont...IJECEIAES
This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.
This document presents a proposed methodology for offline signature recognition using global and grid features extracted from signature images. The methodology involves preprocessing signatures, extracting global and grid features using discrete wavelet transforms, training a backpropagation neural network on the features, and classifying signatures based on the trained network. Experimental results show classification accuracy rates ranging from 89-93% for signatures from 10 to 50 individuals. Future work could involve exploring different signature features to potentially improve recognition performance.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
The document presents a highly secured palm print authentication system using undecimated bi-orthogonal wavelet (UDBW) transform. The proposed system has three main modules: registration, testing, and palm matching. In the registration module, morphological operations and region of interest extraction are used to preprocess palm images. Distance transform and 3-level UDBW transform are then used to extract low-level features and create feature vectors for registered palm prints. In testing, low-level features are extracted from input palm prints using the same approach. Palm matching involves comparing feature vectors of registered and input palm prints to identify matches. Simulation results show the system provides accurate recognition rates for palm print authentication.
A PROJECT REPORT ON IRIS RECOGNITION SYSTEM USING MATLABMaria Perkins
1. Iris recognition is a reliable biometric authentication method that uses the unique patterns in the iris to identify individuals.
2. Previous work has focused on detecting fake irises using techniques like analyzing image quality features, extracting texture features from the iris, and applying weighted local binary patterns.
3. Detecting fake irises using printed contact lenses is challenging but important for security. Methods have analyzed features like iris edge sharpness, iris-texton histograms, and gray-level co-occurrence matrices to differentiate real and fake irises.
4. Combining local descriptors like SIFT with local binary patterns can improve fake iris detection performance by making the approach
This document reviews a technique for combining two fingerprints from different fingers to generate a new combined fingerprint identity for privacy protection. In the enrollment phase, minutiae positions from one fingerprint and orientation from another fingerprint are extracted and combined with reference points from both fingerprints to generate a combined minutiae template. In authentication, the same two fingerprints are matched against the stored template using a two-stage matching process. The combined template cannot be traced back to the original fingerprints, improving anonymity. The combined template can also be reconstructed into a combined fingerprint image to form a new virtual identity matched using standard fingerprint algorithms. The technique aims to protect privacy by preventing full minutiae disclosure of any single fingerprint if the database is stolen.
The document discusses iris recognition technology. It begins by introducing iris recognition as a biometric authentication method using pattern recognition on high-resolution eye images. It then provides details on how iris recognition works, including isolating the iris area in an image, encoding the iris patterns into binary templates, and comparing templates to identify or verify individuals. The document also discusses the statistical properties of iris patterns that make iris recognition highly accurate and reliable compared to other biometric methods. It concludes by mentioning some commercial applications of iris recognition technology.
Similar to A Novel Approach for Non-Invertible Cryptographic Key Generation from Cancellable Finger Print Template (20)
A Novel Approach for Non-Invertible Cryptographic Key Generation from Cancellable Finger Print Template
1. A Novel Approach for Non-Invertible Cryptographic Key
Generation from Cancellable Fingerprint Template
A midterm project report submitted to the Electronics and Communication
Department of RGUKT-Nuzivid in partial fulfilment of the
requirements for the Degree of Bachelor of Technology
By
RAGHAVENDRA GOLI N082312
SREENIVASA RAO PALLAPU N082305
SURYA NARAYANA KONARI N082284
Under the Guidance of
Mr K. Shivlal, M.Tech,
Lecturer in Department of ECE,
Rajiv Gandhi University of Knowledge Technologies,
Nuzvid, Krishna (Dt).
4th
January, 2014
2. ABSTRACT
The difficulty of maintenance of the key and remembering it is the main
drawback associated with the old cryptographic systems. As a result, utilizing
individual’s biometric features in the generation of strong and repeatable
cryptographic keys has gained enormous popularity among researchers. The
unpredictability of the user's biometric features, incorporated into the generated
cryptographic key, makes the key unguessable to an attacker lacking notable
knowledge of the user's biometrics. Nevertheless, if a person’s biometric is lost once,
it will be compromised forever as it is inherently associated with the user.
Cancellable biometrics is a solution for cancelling and re-issuing biometric
templates. The cancellable fingerprint templates are then utilized to generate a
unique noninvertible key. Initially, a one-way transformation is applied on the
minutiae points extracted from the fingerprints, to attain a set of transformed points.
Subsequently, the transformed points are made use of to form cancellable templates.
As the cryptographic key generated is non-invertible, it is highly infeasible to
acquire the cancellable fingerprint templates or the original fingerprint from the
generated key.
3. CONTENTS
ABSTRACT ii
1. INTRODUCTION 1
2. PROPOSED ALGORITHM 2
3. BLOCK DIAGRAM 3
4. PREPROCESSING 4
4.1 Normalization 4
4.2 Orientation Estimation 5
4.2.1 Reliability of Orientation Field 6
4.2.2 Ridge Frequency 7
4.3 Segmentation 7
4.3.1 Histogram-Based method 8
4.3.2 Edge Detection method 8
4.3.2.1 Canny Edge Detection 8
4.3.2.2 Gabor Filter 9
4.4 Ridge Filling and Ridge Filter for smoothing: 10
4.5 Ridge extraction 11
4.6 Thinning 11
5. FEATURE EXTRACTION 12
5.1 Using a 3X3 template mask on the binary thinned fingerprint image 12
5.2 Using 3X3 mask base-on neural network 13
5.3 False Minutiae Removal 14
5.3.1 Algorithm for Removing Minutiae at borders 15
5.3.2 Algorithm for Multiple Minutiae removal 16
6. CANCELLABLE FINGERING TEMPLATE GENERATION 16
7. CONCLUSIONS AND FUTURE WORK 18
REFERENCES 19
4. List of Figures
Figure 1: Normalization (a) Input Image (b) Normalized Image 5
Figure 2 : Orientation Estimation (a) Input Image (b) Orientation field 6
Figure 3: Reliability (a) Input Image (b) Reliability Image 7
Figure 4: Frequency Estimation (a) Input Image (b) Frequency Image 7
Figure 5: Canny Edge Detection (a) Input Image (b) Edge Detected Image 9
Figure 6: Gabor filter (a) Smoothed image (b) Ridge filtered image 10
Figure 7: Binarization (a) Reliability (b) Binary Image after thresholding 11
Figure 8: Thinning (a) Binary Image (b) Thinned Image 12
Figure 9 : 3X3 template mask 13
Figure 10: Feature Extraction (a) Thinned Image (b) Minutiae Extracted Image 13
Figure 11: Patterns obtained after neural network training 14
Figure 12: False Minutiae Removal (a) Normalized Image (b) Mask (c) Thinned image after masking (d)
Thinned image before masking (e) Minutiae extraction before masking (f) Minutiae extraction after false
minutiae removal 15
List of Equations
Equation 1: Normalization mathematical equation 4
Equation 2: Formula for local orientation estimation at each pixel 5
Equation 3: Formula of Canny Detector; determine the edge gradient and direction 8
Equation 4: Gabor filter basic formula 9
Equation 5: Gabor filter imaginary part 9
Equation 6: Thresholding formula 11
5. 1
1. INTRODUCTION
Fingerprint is one of the most well-know and publicized biometrics for
personal identification, because it is unique. Different people have different
fingerprints. Fingerprint has been used as an identification approach for a long time.
Fingerprint recognition means provide an automated method of verifying a match
between two human fingerprints. Today, Fingerprint recognition is widely used in
human life. For example, in security identify a human for accessing a building or
accessing a system. Some personal laptop provides a fingerprint recognition function
to allow a user login.
Fingerprint has been used for human identification for a long time. Human
used fingerprint as their signatures. The modern fingerprint recognition techniques
were invited in the late 16th
century. Henry Fauld, a man who first scientifically
suggested the individuality and uniqueness of fingerprints. After this, foundation
theory of modern fingerprint identification had been established in this century. In
the late 19th
century, Sir Francis Galton conducted an extensive study of fingerprints.
He provided the features for fingerprint classification in 1888. This is the earliest
identification of fingerprint feature. In the early 20th
century, fingerprint recognition
was formally accepted as a personal identification method by many countries. For
example: Garda station, law enforcement agencies. Fingerprints became a standard
procedure in forensics. Today, fingerprint recognition is widely used.
The principal drawback of the existing cryptographic algorithms is the
maintenance of their key’s secrecy. Added with, human users have a difficult time
remembering strong but lengthy cryptographic keys. As a result, utilizing
individual’s biometric features in the generation of strong and repeatable
cryptographic keys has gained enormous popularity among researchers. The
unpredictability of the user's biometric features, incorporated into the generated
cryptographic key, makes the key unguessable to an attacker lacking noteworthy
knowledge of the user's biometrics. Nevertheless, if a person’s biometric is lost once,
it will be compromised forever as it is inherently associated with the user. To
overcome the above, cancellable biometrics has been proposed as an effective
solution for cancelling and re-issuing biometric templates.
6. 2
In recent times, researchers have focused on incorporating biometrics with
cryptography as a possible way to enhance overall security by purging the necessity
of key storage via passwords. Biometric cryptosystems, or crypto-biometric
systems, unite cryptographic security with biometric authentication. In the
cryptographic technique the original data is encoded by using any key so that it is
not in an understandable format for the attacker [1]. The original data can be obtained
by decoding the encoded data using the same key. Thus the privacy is well protected
in this cryptographic approach. Several cryptographic techniques like DES, AES and
public key architectures like RSA are widely used for the authentication purpose.
In this report, corrupted fingerprint image is firstly subjected to pre-processing
and then it is made such that prominent futures in the fingerprint are clearly
intelligible. Then pre-processed fingerprint template is given to a thinning algorithm
for further processing. The features in the fingerprint (minutiae) are extracted from
thinned image.
Finally, A subset of minutiae points are given to cancellable fingerprint
template generation algorithm. And further template is given to crypto-system to
give non-recoverable key.
The rest of the report is organized as follows. Our proposed algorithm is
presented in Section II and block diagram in Section III. The Pre-processing of the
fingerprint image is presented in Section IV. The Extraction of minutiae points from
enhanced fingerprint image is presented in Section V. The transformation of the
minutiae points and the generation of the cancellable fingerprint template from the
transformed minutiae points are discussed in Section VI. The Conclusions and future
work are summed up in Section VII.
2. PROPOSED ALGORITHM
Cancellable fingerprint generation from user given fingerprint includes three
main steps:
I. Pre-processing
II. Feature Extraction
III. Cancellable Fingerprint Generation
7. 3
We implemented Pre-processing block as given literature and developed
proficient algorithms for ridge filling after edge detection block. We implemented
efficient algorithm for false minutiae and multiple minutiae removal.
3. BLOCK DIAGRAM
I/P: User Fingerprint Image
O/P: Cancellable fingerprint template
Normalization
Orientation
Estimation
Segmentation
Thinning
Ridge
Extraction
Ridge Filling
Pre-Processing Module
Feature Extraction Module
3x3 template
mask method
Border
False
Minutiae
removal
Multiple
Bifurcation
removal
Cancellable Fingerprint Template Module
Template
Generation
Algorithm
8. 4
4. PREPROCESSING [2]
A critical step in cancellable fingerprint template generation is to
automatically and reliably extract minutiae from the input fingerprint images for
further processing. The fingerprint image is not suitable for minutiae extraction after
we get the image from fingerprint device. However, the performance of a minutiae
extraction algorithm relies heavily on the quality of the input fingerprint images. In
order to ensure that the performance of minutiae extraction module will be robust
with respect to the quality of input fingerprint images, it is essential to incorporate a
fingerprint enhancement algorithm before the minutiae extraction module.
We present a fast & efficient fingerprint enhancement algorithm, which can
adaptively improve the clarity of ridge and valley structures of input fingerprint
images based on the estimated local ridge orientation and frequency of normalized
image. We have evaluated the performance of the image enhancement algorithm
using subjective analysis and the goodness index of the extracted minutiae and the
accuracy of an online fingerprint verification system.
The Pre-processing stage includes normalization, Orientation estimation,
segmentation, ridge extraction and binarization.
4.1 Normalization [3]
Normalization is a pixel-wise operation. It does not change the clarity of the
ridge and valley structures. The main purpose of normalization is to reduce the
variations in gray-level values along ridges and valleys, which facilitates the
subsequent processing steps. The normalized image is defined as the follows
mathematics formula (Equation 1):
( , ) =
⎩
⎨
⎧ +
( ( , ) )
−
( ( , ) )
Equation 1: Normalization mathematical equation
If I (i, j)>M
Otherwise
9. 5
In this formula, I(i,j) means the gray level of point (i, j). M0 and VAR0 are the
desired mean and variance values. Figure 12 shows an example of image
normalization.
(a) (b)
Figure 1: Normalization (a) Input Image (b) Normalized Image
4.2 Orientation Estimation [3]
An orientation field represents the directionality of ridges in the fingerprint
image. It is a very important role in fingerprint image analysis. This step is a basic
step for minutiae extraction. It also prepare for image segmentation. “Fingerprint
image is typically divided into a number of non-overlapping blocks (e.g. 32x32
pixels) and an orientation representative of the ridges in the block is assigned to the
block based on an analysis of grayscale gradients in the block. The block orientation
could be determined from the pixel gradient orientations based on, say, averaging,
voting, or optimization”.
The following steps show the processing of orientation estimation:
Divide the input fingerprint image into blocks of size WxW.
Compute the gradients Gx and Gy at each pixel in each block.
Estimate the local orientation at each pixel (i, j) using the equations below
( , ) = 2 ( , ) ( , ),
/
/
/
/
( , ) = ( ( , ) − ( , )),
/
/
/
/
( , ) =
1
2
tan
( , )
( , )
Equation 2: Formula for local orientation estimation at each pixel
10. 6
Where W is the size of the local window; Gx and Gy are the gradient
magnitudes in x and y directions, respectively.
Compute the consistency level of the orientation field in the local neighbourhood
of a block(i, j) with the following formula in figure
If the consistency level is above a certain threshold Th, then the local orientations
around this region are re-estimated at a lower resolution level until C(i, j) is
below a certain level.
Figure 2 : Orientation Estimation (a) Input Image (b) Orientation field
4.2.1 Reliability of Orientation Field [2]
It is a measure of reliability of the orientation measure. This is a value between
0 and 1. The value above 0.5 can be considered ‘reliable’. Reliability is used to find
out non-recoverable regions even after enhancement.
11. 7
Figure 3: Reliability (a) Input Image (b) Reliability Image
4.2.2 Ridge Frequency
Ridge Frequency algorithm is to estimate the fingerprint ridge frequency
across a fingerprint image. This is done by considering blocks of the image and
determining a ridge count within each block.
Figure 4: Frequency Estimation (a) Input Image (b) Frequency Image
4.3 Segmentation
Image segmentation is a basic way for fingerprint image enhancement. We
cannot extract features from a fingerprint image without image enhancement,
because without image segmentation, some important features will not present
clearly, some unimportant features will present, some features maybe present twice.
All these will lead to a false feature extraction. Segment is a way for keeping the
useful image information and removes the un-useful image information. Image
segmentation is typically used to locate objects and boundaries in images.
There are mainly two methods in Segmentation
12. 8
4.3.1 Histogram-Based method
Histogram-based methods are very efficient when compared to other image
segmentation methods because they typically require only one pass through the
pixels. In this technique, a histogram is computed from all of the pixels in the image,
and the peaks and valleys in the histogram are used to locate the clusters in the image.
In this method, image has been divided into several blocks. Using a gray level
wavelet histogram to presents each block gray level, so that, we can determine how
many blocks are useful (How many blocks are in the accepting gray level). With this
method, we can keep the useful information part in the image, but it has a
disadvantage. Its disadvantage is that it may be difficult to identify significant peaks
and valleys in the image. [4]
4.3.2 Edge Detection method
Edge Detection algorithms are useful in fingerprint segmentation in
identifying points in a digital image at which the image brightness changes sharply
or more formally has discontinuities. [5]
Through edge detection, we can reduce amount of data and throw away
information which is not used for fingerprint analysis. The idea underlying most
edge-detection techniques is on the computation of a local derivative operator such
as “Roberts”, “Prewitt”, “Canny” or “Sobel” operators.
4.3.2.1 Canny Edge Detection [6]
An edge in an image may point in a variety of directions, so the canny
algorithm uses four filters to detect horizontal, vertical and diagonal edges in the
blurred image. The edge detection operator (Roberts, Prewitt, Sobel for example)
returns a value for the first derivative in the horizontal direction (Gy) and the vertical
direction (Gx). From this the edge gradient and direction can be determined as
follows
Equation 3: Formula of Canny Detector; determine the edge gradient and direction
13. 9
The edge direction angle is rounded to one of four angles representing vertical,
horizontal and the two diagonals.
Figure 5: Canny Edge Detection (a) Input Image (b) Edge Detected Image
4.3.2.2 Gabor Filter
Gabor filter is a linear filter used for edge detection. It has been found to be
particularly appropriate for texture representation and discrimination.
Its impulse response is defined by a harmonic function multiplied by a
Gaussian function. Because of the multiplication-convolution property (Convolution
theorem), the Fourier transform of a Gabor filter's impulse response is the
convolution of the Fourier transform of the harmonic function and the Fourier
transform of the Gaussian function. The filter has a real and an imaginary component
representing orthogonal directions the two components may be formed into a
complex number or used individually.
Real:
Equation 4: Gabor filter basic formula
Imaginary:
Equation 5: Gabor filter imaginary part
Where,
14. 10
And
In this equation, λ represents the wavelength of the sinusoidal factor, θ represents
the orientation of the normal to the parallel stripes of a Gabor function, ψ is the phase
offset, σ is the sigma of the Gaussian envelope and γ is the spatial aspect ratio, and
specifies the ellipticity of the support of the Gabor function.
These two algorithms can be used in edge detection. Canny edge detector is
traditional way and Gabor filter seems more suitable for do the edge detection in
the image which includes character only, but some research papers say edge
detection using Gabor filter is accurate in fingerprint edge detection, so here we
present both canny edge detection and then to use Gabor filter in our program.
4.4 Ridge Filling and Ridge Filter for smoothing:
After edge detection, we can get an image that: reduce amount of un-useful
data. But still it is not sufficient for further processing because small portions of
ridges may be broken after edge detection leading to false minutiae. Ridge filling
algorithm, by using pixel adjacency can fill up the gaps in ridges to reduce false
minutiae points.
After that, Ridge filter algorithm is used to smooth out the segmented image
by using Orientation image and ridge frequency. Reliability of Orientation is used
to remove non-recoverable regions after smoothing. Finally, we will get smoothed
image for thinning process.
Figure 6: Gabor filter (a) Smoothed image (b) Ridge filtered image
15. 11
4.5 Ridge extraction [3]
The important property of the ridges in a fingerprint image is that gray level
values on ridges. There is an algorithm was provided base on gray level threshold.
This algorithm is image binarization [7]. The approaches to ridge extraction use
either simple or adaptive threshold. With this way, we can separate the foreground
(Fingerprint part) and the background. The theory of this algorithm is that: calculate
a gray level threshold, compare this threshold to each pixel of the fingerprint image.
Below equation is the formula of this algorithm.
=
1 [ ] ≥ [ ]
0 ℎ
Equation 6: Thresholding formula
Where iVal is point i, Gmean[I] is this point’s gray level, Gmean[iVar] is the gray
level threshold. With this way, the foreground’s gray level will be set in 1, and
background’s gray level will be set in 0. The ridge can be extracted. After this,
fingerprint image become a binary image. Below figure is an example of result
image.
Figure 7: Binarization (a) Reliability (b) Binary Image after thresholding
4.6 Thinning
After Ridge extraction, the image still cannot be used for minutiae extraction,
because the ridge is too wide, it is not suitable for extract feature points. We have to
make the ridge thin. Thinning is to representing the structural shape of a plane region
16. 12
is to reduce it to a graph. This reduction may be accomplished by obtaining the
skeleton of the region via thinning.
Figure 8: Thinning (a) Binary Image (b) Thinned Image
5. FEATURE EXTRACTION
After pre-process, a fingerprint image is prepared for minutiae extraction. A
fingerprint is characterized by a pattern of interleaved ridges (dark lines) and valleys
(bright lines). Generally, ridges and valleys run in parallel and sometimes they
terminate or they bifurcate. At a global level, the fingerprint may present regions
with patterns of high curvature; these regions are also called singularity. At the local
level, other important feature called minutia can be found in the fingerprint patterns.
Minutia mean small details and this refers to the behaviour of the ridges
discontinuities such as termination, bifurcation and trifurcation or other features
such as pores (small holes inside the ridges), lake (two closed bifurcations), dot
(short ridges), etc. I prefer to match the minutiae for comparing, so that, I will only
describe the minutiae extraction in local level. There are two algorithms for minutiae
extraction.
5.1 Using a 3X3 template mask on the binary thinned fingerprint image
Here, we use a 3X3 template mask (Figure 9) to extract the minutiae. The
main process of this algorithm is that:
17. 13
In figure 9, (x, y) is a point which is waiting for determine. N1 to N8 is 8
points which nearby the point. Searching this mask from top to down, left to right;
and then calculating the translation of “0” and “1”.
Steps:
If the translation count is 4
Point(x, y) is a bifurcation minutia;
If the translation count is 2
Point(x, y) is an ending minutia;
Else,
Point(x, y) is not minutia;
Figure 9 : 3X3 template mask
3X3 template mask
Figure 10: Feature Extraction (a) Thinned Image (b) Minutiae Extracted Image
5.2 Using 3X3 mask base-on neural network [8]
This algorithm is based on neural network algorithm. The minutiae are
detected by using 3X3 masks. All this masks used for identifying the ridge ending
and bifurcations point. Before minutiae extraction, we have to train the network
will all the masks. Once the neural network is trained, next step is to input the
prototype fingerprint image to extract the minutiae, using these masks to scan the
fingerprint image.
18. 14
Figure 11: Patterns obtained after neural network training
5.3 False Minutiae Removal
After minutiae extraction, some of the minutiae points exists at the borders
of the fingerprint image and some minutiae may be present because of loops
existed in thinned image. These minutiae points may not be present in original
fingerprint image. So, we have to remove those from final thinned image before
feature extraction.
A novel approach of making mask image from normalized image in such a
way that non-ridge regions in normalized image makes mask image which when
applied to thinned image will remove false minutiae points at the borders of
fingerprint image.
19. 15
After minutiae extraction process, we may also get multiple bifurcation and
multiple ridge ending points, we can remove those points by considering
bifurcation point’s image and applying multiple bifurcation removal algorithm we
developed. Finally, we will get minutiae extracted image almost removing all false
minutiae points for generation of cancellable fingerprint generation.
Figure 12: False Minutiae Removal (a) Normalized Image (b) Mask (c) Thinned image after
masking (d) Thinned image before masking (e) Minutiae extraction before masking (f)
Minutiae extraction after false minutiae removal
5.3.1 Algorithm for Removing Minutiae at borders
Steps followed:
Divide normalized image into 30 blocks giving block size 10x10
Binarize the block obtained using threshold 128 resulting binary block
Find the sum of pixel values in binary block
If sum of pixel values is greater than threshold (ex: 95 for 10x10 block)
o Make block as non-ridge region
Else
20. 16
o Make block as ridge region
5.3.2 Algorithm for Multiple Minutiae removal
Steps followed:
Scan the bifurcation points from left to right and top to bottom
Intensity value of pixel is I(x, y)
If pixel value is 0
o Apply bifurcation mask at the pixel
o If number of 0’s in mask is 2
Point(x, y) is an ending minutiae
o If number of 0’s in mask is 4
Point(x, y) is bifurcation minutiae
And remove m-connected bifurcation at point(x, y)
Else
o Move to next pixel
6. CANCELLABLE FINGERING TEMPLATE GENERATION
[9] [10]
This module transforms the extracted minutiae points into transformed points
and the generation of cancellable fingerprints. The extracted minutiae points are
represented as
And their equivalent x, y coordinates are specified as
These x, y co-ordinates are symbolized as a vector and transformed
completely into another set of transformed points with the aid of the deterministic
algorithm discussed. To begin with, the x, y co-ordinates of the minutiae points are
stored in a vector VC. For each element in the vector VC the corresponding next
prime number is obtained and placed in another vector VP. Then, a discrete
exponential function is applied on individual elements of VC with their
21. 17
corresponding values in VP. If the discrete exponential value ED computed is prime,
then the value is appended to a vector PDE, else the corresponding next prime
number is obtained and appended to PDE.
The following steps are involved in the formation of the transformed points
from the vector PDE:
Random pair selection [9]: The indexes for random selection of pairs
from PDE are computed by the below mathematical operation. The
random pairs selected are removed from PDE and the process is
repeated until PDE is empty.
Prime factoring [9]: The pair of values selected is prime numbers and
represented as (R1, R2). The values in each pair are multiplied to obtain
the transformed points. The pairs taken out from PDE are represented
as
The transformed points are denoted as
As the two values Ri1 and Ri2 are prime numbers, the multiplication
results in a value that is almost infeasible to factorize. The utilization
of prime number factoring and discrete exponential guarantees that,
obtaining minutiae points’ co-ordinates from the transformed points is
extremely complex. Subsequently, the distance between each point
with respect to the other points is computed
22. 18
After the calculation of the respective distances of each point, the
values are sorted in a separate array and unique values are taken out.
The array is represented as:
And the values obtained are denoted as
Sorted array is represented as SD=Sort (D)Asc
Whereas the unique values are represented as
The UD thus created is termed as the “cancellable fingerprint
template” [10]. The cancellable template UD is employed in the
generation of non-invertible cryptographic key.
7. CONCLUSIONS AND FUTURE WORK
While fingerprint biometrics presents obvious advantages over password and
token-based security, the difficulty in assuring the integrity of the key is one of the
most important problems associated with cryptosystems. Generating cryptographic
keys from cancellable biometrics has received considerable attention in recent years.
We outlined several advances that originated both from the cryptographic encryption
of cancellable fingerprint biometrics to address problem of compromised fingerprint
database. In particular, we outlined the advantages of cancellable biometrics over
other approaches and presented a proficient approach for enhancing uneven
fingerprint images and proposed an efficient algorithms for filling up the gaps in
ridge structures after segmentation and to remove multiple minutiae from thinned
image.
For future work, we intend to develop an algorithm for unique cancellable
fingerprint template generation irrespective of the orientation of user given
23. 19
fingerprint image. We would also present different proficient algorithms for
generation of cryptographic key from generated cancellable fingerprint.
REFERENCES
[1] An Efficient Approach For Non-Invertible Cryptographic Key Generation
From Cancellable Fingerprint Biometrics, N. Lalithamani, Dr. K.P. Soman,
AMRITA Vishwa Vidyapeetham, Coimbatore
[2] Fingerprint Image Enhancement: Algorithm and Performance Evaluation Lin
Hong, Student Member, IEEE, Yifei Wan, and Anil Jain, Fellow, IEEE.
[3] Research Manual Fingerprint Recognition, Nigel Whyte and Dayu Chen,
INSTITUE OF TECHNOLOGY CARLOW.
[4] Wikipedia. (2010). Segmentation, available:
http://en.wikipedia.org/wiki/Segmentation (image processing).
[5] Wikipedia. (2010). Edge detection, available:
http://en.wikipedia.org/wiki/Edge_detection.
[6] Feature Detector – Canny edge detector
http://homepages.inf.ed.ac.uk/rbf/HIPR2/canny.htm#1
[7] Wenzhou Liu, Xiangping Meng, Linna Li and Quande Yuan (2008), A kind of
Effective Fingerprint Recognition Algorithm and Application In Examinee I
dentity recognition available:
ftp://ftp.computer.org/press/outgoing/proceedings/csse08/data/3336d035.pdf
[8] Manvjeet Kaur, Mukhwinder Singh, Akshay Girdhar, Parvinder S. Sanhu
(2008).Fingerprint verification system using minutiae extraction
techniqueavailable:
http://www.waset.org/journals/waset/v46/v46-85.pdf
[9] “RSA Factoring Challenge” from
http://en.wikipedia.org/wiki/RSA_Factoring_Challenge
[10] N. Lalithamani, K.P. Soman, "An Effective Scheme for Generating
Irrevocable Cryptographic Key from Cancelable Fingerprint Templates",
International Journal of Computer Science and Network Security, Vol.9, No.3, pp:
183- 193, 2009.