Your search
Results 4 resources
-
We give the theoretical foundation for finding a reject region which gives the minimum equal error rate in serial fusion based biometric verification. Given a user-specified tolerance of x percent genuine score reject rate, we prove that there exists a unique reject region inside which the false alarm rate and impostor pass rate curves overlap, and this reject region gives the minimum equal error rate. Our theory leads to new algorithms for finding reject regions, which have two key advantages over the state-of-the-art: (1) the algorithms allow the system administrator to control the proportion of genuine scores that a reject region can erroneously reject and (2) the algorithms determine reject regions directly from the scores, without the need to estimate score distributions. Our proofs do not rely on data belonging to any particular distribution, which makes them applicable to a wide range of biometric modalities including face, finger, iris, speech, gait, and keystrokes. © 2016 IEEE.
-
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature - all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found that certain features, such as punctuation and various suffixes result in a higher accuracy. In addition, the Support Vector Machine classifier produces repeatedly higher accuracies than other classifiers and seems to be a far superior method of classification in terms of authorship categorization. © 2016 IEEE.
-
The traditional architecture of serial fusion based multi-biometric verification systems places an average performing or the worst performing individual verifier in the final stage. Because the final stage gives the verification decision using a single threshold and takes on the most confusing samples which are rejected by all previous stages, an average or the worst performing individual verifier may incur high verification errors in the final stage, which may negatively impact the performance of the whole system. Unfortunately, it is not possible to place a strong individual verifier in the final stage of a traditional architecture because if we place a strong individual verifier in the final stage, we will have to place a weak individual verifier in an earlier stage. Studies show that placing a weak individual verifier in an earlier stage worsens the performance of the whole system by giving more wrong decision earlier. Hence, the challenge is-how can we place the best performing individual verifier in the first stage and at the same time not place an average or the worst performing individual verifier in the final stage? In this paper, we address this challenge. We have come up with a very simple but effective solution. We have proposed a modification to the traditional architecture of serial fusion based multi-biometric verification systems. With rigorous experiments on the NIST multi-modal dataset and using three serial fusion based multi-biometric verification schemes, we demonstrated that our proposed architecture significantly improves the performance of serial fusion based multi-biometric verification systems. © 2018 IEEE.
-
Smartphones, while providing users ease of access to sensitive information on the go, also present severe security risks if an attacker is able to gain access to them. To strengthen the user authentication and identification in a smartphone, we develop a biometric authentication and identification system which uses the capacitive touchscreen that is featured in all current smartphones. Our methodology focuses on using the touchscreen as a sensor to capture the image of a user's ear, thumb or four fingers. We extract the capacitive raw data from the touched body part to obtain a capacitive image, and then use it to capture geometric features (e.g., length and width of a finger) and principal components. After that, we experiment with Support Vector Machine (SVM) and Random Forest (RF) classifiers to verify and also identify each user. We achieved the maximum authentication accuracy of 98.84% by four fingers with SVM, and maxinum identification accuracy of 97.61% by four fingers with RF. © 2016 IEEE.