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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
We propose a new method in rank level fusion for biometric identification. Our method is based on the pool adjacent violators (PAV) algorithm after the ranks have been transformed to the approximated scores. We then show that our method outperforms various approaches that commonly used in biometric rank level fusion on NIST BSSR1 multimodal database.
In scenarios that are ambitious to protect sensitive data in compliance with privacy regulations, conventional score normalization utilizing large proportions of speaker cohort data is not feasible for existing technology, since the entire cohort data would need to be stored on each mobile device. Hence, in this work we motivate score normalization utilizing deep neural networks. Considering unconstrained...
When the performance of a classifier is empirically evaluated, the Area Under Curve (AUC) is commonly used as a one dimensional performance measure. In general, the focus is on good performance (AUC towards 1). In this paper, we study the other side of the performance spectrum (AUC towards 0.50) as we are interested to which extend a classifier is random given its AUC. We present the exact probability...
This paper presents a bimodal scheme - the mechanism which exploits the way the user enters her 8-digit PIN/password and the phone-movements while doing so, for user authentication in mobile banking/financial applications (apps). The scheme authenticates the user based on the timing differences of the entered strokes. Additionally, it provides an enhanced security by adding an unobservable layer based...
It is widely known that biometric systems based on adults fingerprints have reached an outstanding performance when compared against other biometric traits. This explains their extensive use by governmental agencies in charge of citizen identification. Nevertheless, the performance is highly degraded when fingerprints of newborns or toddlers are used. In this work, we analyze the performance of existing...
This paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feed-forward neural network classifiers...
In this paper we propose a biometric recognition system based on steady-state visual evoked potentials (SSVEPs), exploiting brain signals elicited by repetitive stimuli having a constant frequency as identifiers. EEG responses to SSVEP stimuli flickering at different frequencies are recorded, and both mel-frequency cepstral coefficients (MFCCs) and autoregressive (AR) reflection coefficients are used...
Gait recognition is a technique that identifies or verifies people based upon their walking patterns. Smartwatches, which contain an accelerometer and gyroscope have recently been used to implement gait-based biometrics. However, this prior work relied upon data from single sessions for both training and testing, which is not realistic and can lead to overly optimistic performance results. This paper...
The performance of a biometric system gets affected by various types of errors such as systematic errors, random errors, etc. These kinds of errors usually occur due to the natural variations in the biometric traits of subjects, different testing, and comparison methodologies. Neither of these errors can be easily quantifiable by mathematical formulas. This behavior introduces an uncertainty in the...
Estimation of orientation field is a crucial issue when processing fingerprint samples. Many subsequent fingerprint processing steps depend on reliable and accurate estimations. Algorithms for such estimations are usually evaluated against ground truth data. As true ground truth is usually not available, human experts need to mark-up ground truth manually. However, the accuracy and the reliability...
De-duplication is defined as the technique to eliminate or link duplicate copies of repeating data. We consider a specific de-duplication application where a subject applies for a new passport and we want to check if he possesses a passport already under another name. To determine this, a facial photograph of the subject is compared to all photographs of the national database of passports. We investigate...
This work targets people identification in video based on the way they walk (i.e. gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the...
In the last years, several papers on EEG-based biometric recognition systems have been published. Specifically, most of the proposed contributions focus on brain signals recorded in resting state conditions, with either closed or open eyes. A common assumption is that the acquired signals are quasi-stationarity. In this paper, we investigate such property in terms of discriminative capability, and...
In the past few decades, automatic face recognition has been an important vision task. In this paper, we exploit the spatial relationships of facial local regions by using a novel deep network. In the proposed method, face is spatially scanned with spatial long short-term memory (LSTM) to encode the spatial correlation of facial regions. Moreover, with facial regions of various scales, the complementary...
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