Original Biometric Image Research Articles

CBRW: a novel approach for cancelable biometric template generation based on 1-D random walk.

Cancelable Biometric is a challenging research field in which security of an original biometric image is ensured by transforming the original biometric into another irreversible domain. Several approaches have been suggested in literature for generating cancelable biometric templates. In this paper, two novel and simple cancelable biometric template generation methods based on Random Walk (CBRW) have been proposed. By employing random walk and other steps given in the proposed two algorithms viz. CBRW-BitXOR and CBRW-BitCMP, the original biometric is transformed into a cancelable template. The performance of the proposed methods is compared with other state-of-the-art methods. Experiments have been performed on eight publicly available gray and color datasets i.e. CP (ear) (gray and color), UTIRIS (iris) (gray and color), ORL (face) (gray), IIT Delhi (iris) (gray and color), and AR (face) (color). Performance of the generated templates is measured in terms of Correlation Coefficient (Cr), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Mean Absolute Error (MAE), Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). By experimental results, it has been proved that proposed methods are superior than other state-of-the-art methods in qualitative as well as quantitative analysis. Furthermore, CBRW performs better on both gray as well as color images.

Biometric iris templates security based on secret image sharing and chaotic maps

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template.

Masquerade attack on biometric hashing via BiohashGAN

Masquerade attack on biometric hashing, which reconstructs the original biometric image from the given hashcode, has been given much attention recently. It is mainly used to validate the security of biometric recognition system or expand existing biometric databases like face or iris. However, an existing state-of-the-art method tends to ignore the perceptual quality of synthesized biometric images in the attack, and consequently, the synthetic images can be easily differentiated from real images. To obtain the high-perceptual-quality image which can simultaneously pass the validation of recognition system, we introduce a new target combining semantic invariability in hashing space and perceptual similarity in biometric space. In order to simulate the mapping from images to hashcodes and tackle the derivative problem related to discrete hashcodes in hashing space, we propose a DNN-based network named SimHashNet. Then we incorporate the SimHashNet into a generative adversarial network as our model named BiohashGAN to generate synthetic images form hashcodes. Experiment result on dataset CASIA-IrisV4.0-Interval and CMU PIE demonstrates that the synthetic images obtained from our model can pass the validation of recognition system and simultaneously maintain high perceptual quality.

An Efficient GCD-Based Cancelable Biometric Algorithm for Single and Multiple Biometrics

Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things (IoT) networks. The objective of using cancelable biometrics is to save the original ones from hacking attempts. A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications. The original biometric is blurred with two co-prime operators. Hence, it can be recovered as the Greatest Common Divisor (GCD) between its two blurred versions. Minimal changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD operation. Hence, the ability to change cancelable templates is guaranteed, since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric image. Furthermore, we test the utility of the proposed algorithm in the single- and multi-biometric scenarios. The multi-biometric scenario depends on compressing face, fingerprint, iris, and palm print images, simultaneously, to generate the cancelable templates. Evaluation metrics such as Equal Error Rate (EER) and Area and Receiver Operator Characteristic curve (AROC) are considered. Simulation results on single- and multi-biometric scenarios show high AROC values up to 99.59%, and low EER values down to 0.04%.

Cancelable Biometric Transformations for E-Passport Security

E-Passport or Electronic Passport is comprised of a microprocessor chip which contains biometric data used to authenticate the identity of passport holder. Facial image, fingerprint image and palm-print image are some of the currently standardized biometric modalities, embedded in the E-Passports. The privacy of the biometric data becomes a problem during storage and transmission. The biometric data cannot be revoked or re-issued unlike tokens or passwords, if compromised. The possibility of producing cancelable templates from the biometric features of user has been studied, thus eliminating threats to privacy and security of the biometric features. Cancelable template is obtained from original biometric image after intentional/repeated distortions (based on user-specific key), and thereby providing security to the templates. The distortions can be implemented either at signal or at feature level. The cancelable templates can be revoked or re-issued whenever required just by changing the user-specific key. The probability of applying cancelable transformations to secure the privacy of the biometric features in E-passport has been explored in the article.

RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognition

Biometrics are being increasingly used across the world, but it also raises privacy and security concerns of the enrolled identities. The main reason is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. Cancelable biometric template is a solution to this problem which can be reissued if compromised. In this paper, we suggest a simple and powerful method called Random Permutation Locality Preserving Projection (RP-LPP) for Cancelable Biometric Recognition. Here, we exploit the mathematical relationship between the eigenvalues and eigenvectors of the original biometric image and its randomly permuted version is exploited for carrying out cancelable biometric recognition. The proposed technique work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The effectiveness of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The advantages of proposed technique are (i) the classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation, (ii) security and quality of generated templates and (iii) robustness across different biometrics. In addition, no image registration is required for performing recognition.

Cancelable Biometrics: a comprehensive survey

Biometric recognition is a challenging research field but suffers from privacy and security concerns. To address this concern, Cancelable Biometrics is suggested in literature in which a Biometric image of a sample is distorted or transformed in such a manner that it becomes difficult to obtain the original Biometric image from the distorted one. Another important characteristic of Cancelable Biometrics is that it can be reissued if compromised. In this research paper, we present a comprehensive survey of more than 120 techniques suggested by various researchers from time to time for Cancelable Biometrics and a novel taxonomy for the same is developed. Further, various performance measures used in Cancelable Biometrics are reviewed and their mathematical formulations are given. Cancelable Biometrics also suffer from various security attacks as given in literature. A review of these security attacks is carried out. We have also performed a review of databases used in literature for nine different Cancelable Biometrics viz. Face, Iris, Speech, Fingerprint, Signature, Palmprint, ECG, Palmvein and Fingervein. Lastly, we have also given future research directions in this field. This study shall be useful for the researchers and practitioners working in this fascinating research area.