The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Authentication via keystroke dynamics
Proceedings of the 4th ACM conference on Computer and communications security
Soft biometrics-combining body weight and fat measurements with fingerprint biometrics
Pattern Recognition Letters
Handbook of Biometrics
GREYC keystroke: a benchmark for keystroke dynamics biometric systems
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Keystroke dynamics with low constraints SVM based passphrase enrollment
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Gender recognition: A multiscale decision fusion approach
Pattern Recognition Letters
Facial marks: soft biometric for face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Spatial Gaussian mixture model for gender recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Face recognition using gender information
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Gait Components and Their Application to Gender Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
User classification for keystroke dynamics authentication
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Keystroke dynamics allows to authenticate individuals through their way of typing on a computer keyboard. In this study, we are interested in static shared secret keystroke dynamics (all the users type the same password). We present new soft biometrics information which can be extracted from keystroke typing patterns: the gender of the user. This is the first study, to our knowledge, experimenting such kind of information in the field of keystroke dynamics. We present a method for gender recognition through keystroke dynamics with more than 91% of accuracy, on the tested dataset, and we show the improvement on keystroke dynamics authentication method using such kind of information through pattern and score fusion. We obtain a gain of 20% when using gender information against a classical keystroke dynamics method.