A statistical approach towards performance analysis of multimodal biometrics systems

Fueled by recent government mandates to deliver public functions by the use of biometrics, multimodal biometrics authentication has made rapid progress over the past a few years. Performance of multimodal biometrics systems plays a crucial role in government applications, including public security and forensic analysis. However, current performance analysis is conducted without considering the influence of noises, which may result in unreliable analytical results when noise levels change in practice. This thesis investigates the application of statistical methods in performance analysis of multimodal biometric systems. It develops an efficient and systematic approach to evaluate system performance in different situations of noise influences. Using this approach, 126 experiments are conducted with the BSSR1 dataset. The proposed approach helps to examine the performance of typical fusion methods that use different normalization and data partitioning techniques. Experiment results demonstrate that the Simple Sum fusion method working with the Min-Max normalization and Re-Substitution data partitioning yields the best overall performance in different noise conditions. In addition, further examination of the results reveals the need of systematic analysis of system performance as the performance of some fusion methods exhibits big variations when the level of noises changes and some fusion methods may produce very good performance in some application though normally unacceptable in others

Multimodal biometrics score level fusion using non-confidence information

Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above

Performance analysis of multimodal biometric systems – An automated statistical approach

This thesis proposes to study and extend the ability of the statistical methodologies that have been established to measure the performance of multimodal biometric systems. In particular, it takes into account the various noise factors that are inevitable in a real world scenario, which influence the performance of biometric systems. The work completed in the past uses the Design of Experiment framework to create a systematic approach to test the performance of biometric systems. Input parameters are varied including the data fusion methods and the normalization schemes (both controlled), and using discrete intervals based deviations in the matching scores (uncontrolled) of genuine and impostor users to represent noise. This work however, is limited provided the manual interface to the developed application. All parameters are fixed and operate over a comparatively small dataset. Further, the design of the existing application limits the extensibility of the same to incorporate additional data sources, increase or decrease the deviation values that contribute to the noise, and generate analytical graphs and reports. It is the purpose of this thesis to establish a framework that is scalable to accommodate additional biometric databases for a larger subject pool. The developed application will also allow users to identify a larger set of deviation values for noise, automatically generate test cases for all possible biometric modalities defined within the system, etc. It is also the intent to provide, as results, the ability for the user to choose from a set of possible graphs and reports that are in tune with the common industry (commercial) standards as opposed to purely technical reports

Generic multimodal biometric fusion

Biometric systems utilize physiological or behavioral traits to automatically identify individuals. A unimodal biometric system utilizes only one source of biometric information and suffers from a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks and unacceptable error rates. Multimodal biometrics refers to a system which utilizes multiple biometric information sources and can overcome some of the limitation of unimodal system. Biometric information can be combined at 4 different levels: (i) Raw data level; (ii) Feature level; (iii) Match-score level; and (iv) Decision level. Match score fusion and decision fusion have received significant attention due to convenient information representation and raw data fusion is extremely challenging due to large diversity of representation. Feature level fusion provides a good trade-off between fusion complexity and loss of information due to subsequent processing. This work presents generic feature information fusion techniques for fusion of most of the commonly used feature representation schemes. A novel concept of Local Distance Kernels is introduced to transform the available information into an arbitrary common distance space where they can be easily fused together. Also, a new dynamic learnable noise removal scheme based on thresholding is used to remove shot noise in the distance vectors. Finally we propose the use of AdaBoost and Support Vector Machines for learning the fusion rules to obtain highly reliable final matching scores from the transformed local distance vectors. The integration of the proposed methods leads to large performance improvement over match-score or decision level fusion

Continuous User Authentication Using Multi-Modal Biometrics

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data

KBOC: Keystroke Biometrics OnGoing Competition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents the first Keystroke Biometrics Ongoing evaluation platform and a Competition (KBOC) organized to promote reproducible research and establish a baseline in person authentication using keystroke biometrics. The ongoing evaluation tool has been developed using the BEAT platform and includes keystroke sequences (fixedtext) from 300 users acquired in 4 different sessions. In addition, the results of a parallel offline competition based on the same data and evaluation protocol are presented. The results reported have achieved EERs as low as 5.32%, which represent a challenging baseline for keystroke recognition technologies to be evaluated on the new publicly available KBOC benchmarkA.M. and M. G.-B. are supported by a JdC contract (JCI-2012- 12357) and a FPU Fellowship from Spanish MINECO and MCD, respectively. J.M. and J.C. are supported by CAPES and CNPq (grant 304853/2015-1). This work was partially funded by the projects: CogniMetrics (TEC2015-70627-R) from MINECO FEDER and BEAT (FP7-SEC-284989) from E

Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

Personal Authentication System Based Iris Recognition with Digital Signature Technology

Authentication based on biometrics is being used to prevent physical access to high-security institutions. Recently, due to the rapid rise of information system technologies, Biometrics are now being used in applications for accessing databases and commercial workflow systems. These applications need to implement measures to counter security threats.  Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most difficult problem is how to keep biometric data while maintaining the practical performance of identity verification systems. This paper presents a biometrics-based personal authentication system in which a smart card, a Public Key Infrastructure (PKI), and iris verification technologies are combined. Raspberry Pi 4 Model B+ is used as the core of hardware components with an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV/ Python, Keras, and sci-kit learn libraries for feature extraction and recognition is chosen for application development in this project. The implemented system gives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets respectively after training. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. The time of key generation, Signature, and Verification is 5.17sec,0.288, and 0.056 respectively for the NTU iris dataset. This work offers the realistic architecture to implement identity-based cryptography with biometrics using the RSA algorithm