Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Integration of biometrics and steganography: A comprehensive review

The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards

Palm-Print Pattern Matching Based on Features Using Rabin-Karp for Person Identification

Palm-print based individual identification is regarded as an effectual method for identifying persons with high confidence. Palm-print with larger inner surface of hand contains many features such as principle lines, ridges, minutiae points, singular points, and textures. Feature based pattern matching has faced the challenge that the spatial positional variations occur between the training and test samples. To perform effective palm-print features matching, Rabin-Karp Palm-Print Pattern Matching (RPPM) method is proposed in this paper. With the objective of improving the accuracy of pattern matching, double hashing is employed in RPPM method. Multiple patterns of features are matched using the Aho-Corasick Multiple Feature matching procedure by locating the position of the features with finite set of bit values as an input text, improving the cumulative accuracy on hashing. Finally, a time efficient bit parallel ordering presents an efficient variation on matching the palm-print features of test and training samples with minimal time. Experiment is conducted on the factors such as pattern matching efficiency rate, time taken on multiple palm-print feature matching efficiency, and cumulative accuracy on hashing

Finger vein biometric identification using discretization method

Over the past years, finger vein identification has gaining increasing attention in biometrics. It has many advantages as compared to other biometrics such as living-body identification, difficult to counterfeit because it resides underneath the finger skin and noninvasiveness. Finger vein feature extraction plays an important role in finger vein identification. The performance of finger vein identification is highly depending on the meaningful extracted features from feature extraction process. However, most of the works focus on how to extract the individual features and not presenting the individual characteristic of finger vein patterns with systematic representation. This paper proposed an improved scheme of finger vein feature extraction method by adopting discretization method. The extracted features will be represented systematically way in order to make classification task easier and increase the identification accuracy rate. The experimental result shows that the accuracy rate of identification of the proposed framework using Discretization is above 98.0%

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper

Biometrics based privacy-preserving authentication and mobile template protection

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template

Finger vein identification based on transfer learning of AlexNet

Nowadays finger vein-based validation systems are getting extra attraction among other authentication systems due to high security in terms of ensuring data confidentiality. This system works by recognizing patterns from finger vein images and these images are captured using a camera based on near-infrared technology. In this research, we focused finger vein identification system by using our own finger vein dataset, we trained it with transfer learning of AlexNet model and verified by test images. We have done three different experiments with the same dataset but different sizes of data. Therefore, we obtained varied predictability with 95% accuracy from the second experiment

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort

Multi-feature Fusion Menggunakan Fitur Scale Invariant Feature Transform dan Local Extensive Binary Pattern untuk Pengenalan Pembuluh Darah pada Jari

Pengenalan pembuluh darah jari merupakan salah satu area dalam bidang biometrika. Sehingga tahap-tahap dalam proses pengenalan pembuluh darah jari memiliki kesamaan dengan proses pengenalan menggunakan biometrika lain yaitu meliputi pengumpulan citra, praproses, ekstraksi fitur, dan pencocokan. Tingkat keberhasilan dari tahap pencocokan ditentukan oleh pemilihan fitur pembuluh darah jari yang digunakan. Kondisi citra pembuluh darah yang rentan terhadap perubahan skala, rotasi maupun translasi menyebabkan kebutuhan akan fitur yang tahan terhadap kondisi tersebut menjadi hal yang penting. Fitur Scale Invariant Feature Transform (SIFT) adalah fitur yang telah cukup banyak digunakan untuk kasus pencocokan citra serta mampu tahan terhadap degradasi kondisi citra akibat perubahan skala, rotasi maupun translasi. Akan tetapi, fitur SIFT kurang memberikan hasil optimal jika diekstraksi dari citra dengan variasi tingkat keabuan seperti yang disebabkan oleh perbedaan intensitas pencahayaan. Fitur Local Extensive Binary Pattern (LEBP) merupakan fitur yang tahan terhadap variasi tingkat keabuan dengan informasi karakteristik lokal yang lebih kaya dan diskriminatif. Oleh karena itu digunakan teknik fusi untuk memperoleh informasi dari fitur SIFT dan fitur LEBP sehingga diperoleh fitur yang memiliki ketahanan terhadap degradasi kondisi citra akibat perubahan skala, rotasi, translasi, variasi tingkat keabuan seperti yang disebabkan oleh perbedaan intensitas pencahayaan. Penelitian ini mengusulkan multi-feature fusion menggunakan fitur SIFT dan LEBP untuk pengenalan pembuluh darah pada jari. Fitur hasil fusion diproses dengan metode Learning Vector Quantization (LVQ) untuk menentukan apakah citra pembuluh darah jari yang diuji dapat dikenali atau tidak. Dengan menggunakan multi-feature fusion diharapkan mampu representasi fitur yang dapat meningkatkan akurasi dari proses pengenalan pembuluh darah jari meskipun fitur diambil dari citra yang mengalami degradasi. Berdasarkan hasil uji coba diperoleh bahwa penggunaan multi-feature fusion dengan fitur SIFT dan LEBP memberikan hasil yang relatif lebih baik jika dibandingkan dengan hanya menggunakan fitur tunggal. Hal tersebut dapat dilihat dari peningkatan hasil kinerja sistem pada kondisi optimum dengan nilai akurasi sebesar 97,50%, TPR sebesar 0,9400 dan FPR sebesar 0,0128. ========== Finger vein recognition is one of the areas in the field of biometrics. The steps of finger vein recognition has in common with other biometric recognition process which include image acquisition, preprocessing, feature extraction and matching. The success rate of matching stage is determined by the selection of features. The conditions of finger vein images are susceptible to changes in scale, rotation and translation. The need for features that are resistant to these conditions becomes important. Scale invariant Feature Transform (SIFT) feature is a feature that has been quite widely used for image matching case and be able to withstand degradation due to changes in the condition of the image scale, rotation and translation. However, SIFT feature provide less optimal results when extracted from the image with gray level variations such as those caused by differences in lighting intensity. Local Extensive Binary Pattern (LEBP) feature is a feature that has resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to obtain information from SIFT feature and LEBP feature. So that, the feature that has been produced can resist degradation problems such as changes in the condition of the image scale, rotation, translation, and gray level variations which caused by differences in lighting intensity. This study proposes a multi-feature fusion using SIFT and LEBP features for finger vein recognition. This fusion feature will be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image can be x recognized or not. By using a multi-feature fusion, it is expected to get representations of features that can improve the accuracy of the finger vein recognition although the feature is taken from the degraded image. Based on experiment results, finger vein recognition that use multi-feature fusion using integration feature of scale invariant feature transform and local extensive binary pattern provide a better result than only use a single feature. It can be seen from the increase of performance system in optimum condition. The accuracy value can achieve 97.50%, TPR at 0.9400 and FPR at 0.0128