Finger Knuckle Print as Unimodal Fuzzy Vault Implementation

In recent years the security breaches and fraud transactions are increasing day by day. So there is a necessity for highly secure authentication technologies. The security of an authentication system can be strengthened by using Biometric system rather than the traditional method of authentication like Identity Cards (ID) and password which can be stolen easily. A biometric system works on biometric traits and fingerprint has the maximum share in market for providing biometric authentication as it is reliable, consistent and easy to capture. Although the biometric system is used to provide security to many applications but it is susceptible to different types of assaults too. Among all the modules of the biometric system which needs security, biometric template protection has received great consideration in the past years from the research community due to sensitivity of the biometric data stored in the form of template. A number of methods have been devised for providing template protection. Fuzzy vault is one of the cryptosystem based method of template security. The aim of fuzzy vault technique is to protect the precarious data with the biometric template in a way that only certified user can access the secret by providing valid biometric. In this paper, a modified version of fuzzy vault is presented to increase the level of security to the template and the secret key. The polynomial whose coefficients represent the key is transformed using an integral operator to hide the key where the key can no longer be derived if the polynomial is known to the attacker. The proposed fuzzy vault scheme also prevents the system from stolen key inversion attack. The results are achieved in terms of False Accept Rate (FAR), False Reject Rate (FRR), Genuine Acceptance Rate (GAR) by varying the degree of polynomial and number of biometric samples. It was calculated that for 40 users GAR was found to be 92%, 90%, 85% for degree of polynomial to be 3, 4 and 5 respectively. It was observed that increasing the degree of polynomial decreased the FAR rate, thus increasing the security

A wide variety of security applications like computers, laptops, mobile phones, ATM machines, physical access control to property etc. are using biometrics based system for human identification. Today’s Biometrics authentication system can provide highest level of security to the user of the service and the service providers. Today’s uni-modal biometric systems has become mature system and being used for low level to medium level security applications. But, for extremely higher level of security requirements like military base, nuclear plants, research laboratories etc., Finger knuckle image and Retina image based combination can be used, as this combinations provides the highest level of accuracy, low error rates, universality etc. Also the security of Multi-modal biometric templates is very importance as biometric template store vital information of the user. In this research work, Multi-modal Biometric system for higher level security using Finger Knuckle image and Retina image with templates security is developed, experiments are carried out and results are calculated using PolyU database for Finger Knuckle image and DRIVE for Retina images. A Finger Knuckle Image processing starts with Median Filtering, CLAHE method and Canny edge detection. The features extraction is carried using SURF feature method. Once the finger knuckle image template is generated, it is encrypted using RSA algorithm and stored in database. The Retina Image Processing is carried out by applying Green channel followed by Gaussian filtering, CLAHE method is applied on outcome of Gaussian filtering. SURF features set is extracted from retina ROI. Retina image template is generated which is later encrypted using RSA algorithm. The final experiment shows that genuine acceptance rate = 100% is achieved for threshold value 0.35 and False Acceptance Rate is only 1%. The scientific contribution of proposed research work is a multi-biometric system with higher accuracy (GAR is 100%), low error rate (FAR is 1%), better template protection by RSA encryption and efficient hybrid model using finger knuckle image and retina image.

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

Biometric recognition systems provide security to systems by preventing unauthorized access. In recent times, research proved that unprotected biometric systems can be invaded by hackers. A spoofed biometric security can be a great risk as a compromised biometric template can be lost forever. It can also be used to gain unauthorized access into systems. There is the need to protect biometric template from hackers and possible misuse. This study presents various template protection schemes in literature. In addition, this paper reviewed fuzzy vault biocryptosystem template protection scheme by comparing some fuzzy vault schemes in literature based on their False acceptance rates (FAR) and Genuine acceptance rates (GAR) with different degrees of polynomial. The study also reported past studies with the lowest and the highest FAR as 0.00% and 0.87% respectively while the lowest and the highest GAR stand at 72% and 99% respectively.

Biometric methods are among the safest and most secure solutions for identity recognition and verification. One of the biometric features with sufficient uniqueness for identity recognition is the finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with an entropy-based pattern histogram and a set of statistical texture features. The genetic algorithm (GA) is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two datasets called Poly-U FKP and FKP are used for performance evaluation. The proposed method managed to achieve 94.91{%} and 98.5{%} recognition rates on the Poly-U FKP and FKP datasets and outperformed all of the existing methods in this respect. These results demonstrate the potential of this method as a simple yet effective solution for FKP-based identity recognition.

Multimodal biometric systems represent a significant advancement in biometric authentication technology by integrating multiple modalities to enhance accuracy and security. Our proposed system combines electrocardiogram (ECG), fingerprint, and finger knuckle print (FKP) modalities to achieve improved authentication performance, especially suited for high-security applications. The system first uses ECG for liveness detection, ensuring that only genuine users proceed, followed by fingerprint and FKP for authentication. Three individual Siamese Neural Networks were developed, each optimized to extract distinct features from each modality. Data quality was enhanced using preprocessing methods such as noise reduction and normalization, along with data augmentation strategies to improve model robustness. The system was evaluated using a balanced dataset of 50 samples per biometric modality. Individual average accuracies reached 99.54% for ECG, and 100% for both fingerprint and FKP considering 90 subjects. Using weighted average score-level fusion with a priority on fingerprint and FKP, the system achieved an overall accuracy of 99.80%, with a False Acceptance Rate (FAR) of 0.20%, False Rejection Rate (FRR) of 0.21%, Equal Error Rate (EER) of 0.20%, and an F1-Score of 99.80%. These results demonstrate the system's resilience to spoofing and robustness against data variability, offering a highly secure authentication solution with practical applicability in cloud-based high-security environments, such as banking and healthcare.

A combination of cryptographic and biometric systems, by performing specific binding technique on cryptographic key and biometric template, the fuzzy vault framework enhances the security level of current biometric cryptographic systems in terms of hiding secret key and protecting the template. Although the original scheme suggests the use of error-correction techniques (e.g. the Reed–Solomon code) to reconstruct the original polynomial, recent implementations do not share the same point of view. Instead, cyclic redundant code (CRC) is applied to identify the genuine polynomial from a set of candidates due to its simplicity. Within the scope of this study, the authors address a significant flaw of current CRC-based fuzzy vault schemes, which allows the potential of successful blend substitution attack. To overcome this problem, an integration of two novel modules into general fuzzy vault scheme, namely chaff point generator and verifier, is proposed. The new modules are designed to be integrated easily into the existing systems as well as simple to enhance. The proposed scheme can detect any modification in vault and, as a result, eliminate the blend substitution attack to improve general security. Moreover, the experimental results of this study with real-world datasets show an increase in genuine acceptance rates.

The fuzzy vault scheme has recently become popular approaches to biometric template protection. Since the original scheme has been designed to work with unordered biometric features, such a scheme cannot effectively utilize order information. We present in this paper a new fuzzy vault scheme that can effectively utilize the ordered characteristics of biometric features. In this scheme, we develop ordered fuzzy vault encoding and decoding processes in order to utilize the ordered information of the features. This prevents the feature components from cross matching and reduces false acceptance ratio (FAR). Furthermore, the original biometric features (or original template) are transformed into binary features (or secure template) by random transformation. The transformed secure template provides both diversity and revocability. This transform also prevents an adversary from obtaining the original biometric template from the secure template and therefore enhance the secure level of the scheme. Based on the proposed scheme, we design an online authentication application framework implemented using face images. We compare our scheme with two contemporary approaches to verify the effectiveness of this approach. Experimental results show that our scheme is able to achieve an improved performance with several desired properties of an online authentication system.

Online finger-knuckle-print verification for personal authentication

Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.

Matching partial heterogeneous face images to a gallery of visible images is a challenging research problem. This scenario is motivated by a number of surveillance applications such as recognition of subjects at night or in the presence of challenging environmental conditions. Standoff distances may range from a meter to hundred meters. Our latest experiments have shown that a face recognition software recently developed in our research group can be adapted to perform cross spectral matching of partial face images. The images are encoded with Gabor Generalized Local Binary Patterns and Gabor Weber operators and matched by means of a Kullbuck-Leibler metric. Our analysis has shown that three separate face regions such as (1) eyes and nasal bridge, (2) cheeks and nasal tip, and (3) mouth and a part of the chin display similar matching performance. Furthermore, we have evaluated performance of periocular regions. For a short standoff distance of 1.5 meters and a database of 48 classes, matching a Short Wave Infrared (SWIR) periocular region against visible regions resulted in 0.7 Genuine Accept Rate (GAR) at False Accept Rate (FAR) set to 0.01. For a long standoff distance of 106 meters and a database of 48 classes, matching SWIR against visible periocular regions yielded 0.4 GAR at FAR equal to 0.1. For a short standoff distance of 1.5 meters and a database of 200 classes, matching a Medium Wave Infrared (MWIR) periocular region against visible regions resulted in 0.35 GAR at FAR set to 0.1.

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network–the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.

One of the critical steps in designing a secure biometric system is protecting the templates of the users. If a biometric template is compromised, it leads to serious security and privacy threats because unlike passwords, it is not possible for a legitimate user to revoke his biometric identifiers and switch to another set of uncompromised identifiers. This work propose a new cancelable biometric template using visual cryptography (VC) for preserving privacy to biometric data such as finger-vein. The finger vein template is divided into two noisy images, known as shares, using VC, where one share is stored in the system database and the other is kept with the user on a smart card. The private image can be revealed only when both shares are simultaneously available at the same time. The individual share images do not reveal the identity of the private image. We experimentally evaluate performance of the proposed scheme. The evaluation is performed based on the two metrics: (i) False Acceptance Rate (FAR), and (ii) False Rejection Rate (FRR).

Theft of a template is theft of identity. Fingerprint identification is used by forensic department in identifying criminals because of its uniqueness. Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits. Fingerprint identification is commonly employed in Forensic science to support criminal investigations and in biometric systems such as civilian and commercial identification devices. The best way to identify criminals is using fingerprint verification which cannot be replaced. So such fingerprint acquired from the criminal should be safely stored and protected from hackers for future references. This paper mainly deals with protecting the criminal's record (fingerprint template). Templates represent intrinsic information about a criminal. The criminal record (Fingerprint template) should be protected from creation of physical spoof and replacement by imposter's template to gain unauthorized access. Due to these reasons, fool-proof techniques are required for securely storing the criminal fingerprint records. Fuzzy vault is a cryptographic construct that is designed to work with biometric features represented as an unordered set like minutiae points. Template protection is done by transformation based approaches and biometric cryptosystems. During transformation template is stored in a transformed form so that the original biometric template cannot be easily recovered. Fingerprint from the criminal is first preprocessed and minutiae points are extracted. Preprocessing is very essential because the creation of fuzzy vault highly depends on the quality of the input fingerprint image. Then image is binarized followed by thinning and then region of interest (ROI) is calculated finally followed by removal of false minutiae points. The remaining useful minutiae points are used in fuzzy vault generation. A polynomial is evaluated from the secret key which is combined with minutiae points to produce a secure vault. Some dummy points called chaff points are also added to increase the security. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction and the number of chaff points. In the proposed system an even more secured fuzzy vault is generated with combined features of fingerprint and palm print to enhance the security of the template stored.