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

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Database

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems