Complementary Feature Level Data Fusion for Biometric Authentication Using Neural Networks

Data fusion as a formal research area is referred to as multi‐sensor data fusion. The premise is that combined data from multiple sources can provide more meaningful, accurate and reliable information than that provided by data from a single source. There are many application areas in military and security as well as civilian domains. Multi‐sensor data fusion as applied to biometric authentication is termed multi‐modal biometrics. Though based on similar premises, and having many similarities to formal data fusion, multi‐modal biometrics has some differences in relation to data fusion levels. The objective of the current study was to apply feature level fusion of fingerprint feature and keystroke dynamics data for authentication purposes, utilizing Artificial Neural Networks (ANNs) as a classifier. Data fusion was performed adopting the complementary paradigm, which utilized all processed data from both sources. Experimental results returned a false acceptance rate (FAR) of 0.0 and a worst case false rejection rate (FRR) of 0.0004. This shows a worst case performance that is at least as good as most other research in the field. The experimental results also demonstrated that data fusion gave a better outcome than either fingerprint or keystroke dynamics alone

Multi-modal association learning using spike-timing dependent plasticity (STDP)

We propose an associative learning model that can integrate facial images with speech signals to target a subject in a reinforcement learning (RL) paradigm. Through this approach, the rules of learning will involve associating paired stimuli (stimulus–stimulus, i.e., face–speech), which is also known as predictor-choice pairs. Prior to a learning simulation, we extract the features of the biometrics used in the study. For facial features, we experiment by using two approaches: principal component analysis (PCA)-based Eigenfaces and singular value decomposition (SVD). For speech features, we use wavelet packet decomposition (WPD). The experiments show that the PCA-based Eigenfaces feature extraction approach produces better results than SVD. We implement the proposed learning model by using the Spike- Timing-Dependent Plasticity (STDP) algorithm, which depends on the time and rate of pre-post synaptic spikes. The key contribution of our study is the implementation of learning rules via STDP and firing rate in spatiotemporal neural networks based on the Izhikevich spiking model. In our learning, we implement learning for response group association by following the reward-modulated STDP in terms of RL, wherein the firing rate of the response groups determines the reward that will be given. We perform a number of experiments that use existing face samples from the Olivetti Research Laboratory (ORL) dataset, and speech samples from TIDigits. After several experiments and simulations are performed to recognize a subject, the results show that the proposed learning model can associate the predictor (face) with the choice (speech) at optimum performance rates of 77.26% and 82.66% for training and testing, respectively. We also perform learning by using real data, that is, an experiment is conducted on a sample of face–speech data, which have been collected in a manner similar to that of the initial data. The performance results are 79.11% and 77.33% for training and testing, respectively. Based on these results, the proposed learning model can produce high learning performance in terms of combining heterogeneous data (face–speech). This finding opens possibilities to expand RL in the field of biometric authenticatio

A Survey of Biometric Recognition Systems in E-Business Transactions

The global expansion of e-business applications has introduced novel challenges, with an escalating number of security issues linked to online transactions, such as phishing attacks and identity theft. E-business involves conducting buying and selling activities online, facilitated by the Internet. The application of biometrics has been proposed as a solution to mitigate security concerns in e- business transactions. Biometric recognition involves the use of automated techniques to validate an individual's identity based on both physiological and behavioural characteristics. This research focuses specifically on implementing a multimodal biometric recognition system that incorporates face and fingerprint data to enhance the security of e-business transactions. In contrast to unimodal systems relying on a single biometric modality, this approach addresses limitations such as noise, universality, and variations in both interclass and intraclass scenarios. The study emphasizes the advantages of multimodal biometric systems while shedding light on vulnerabilities in biometrics within the e- business context. This in-depth analysis serves as a valuable resource for those exploring the intersection of e-business and biometrics, providing insights into the strengths, challenges, and best practices for stakeholders in this domain. Finally, the paper concludes with a summary and outlines potential avenues for future research

Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Progressive-Regressive Strategy for Biometrical Authentication

This chapter thoroughly investigates the use of the progressive–regressive strategy for biometrical authentication through the use of human gait and face images. A considerable amount of features were extracted and relevant parameters computed for such an investigation and a vast number of datasets developed. The datasets consist of features and computed parameters extracted from human gait and face images from various subjects of different ages. Soft-computing techniques, discrete wavelet transform (DWT), principal component analysis and the forward–backward dynamic programming method were applied for the best-fit selection of parameters and the complete matching process. The paretic and non-paretic characteristics were classified through Naïve Bayes’ classification theorem. Both classification and recognition were carried out in parallel with test and trained datasets and the whole process of investigation was successfully carried out through an algorithm developed in this chapter. The success rate of biometrical authentication is 89%