The genetic traits in the Electroencephalogram (EEG), has made it a possible characteristic for the continuous biometric human verification. It is important for a biometric characteristic to have an acceptable performance on large populations besides satisfying the main requirements of a biometric system such as uniqueness, universality, acceptability, permanence, collectability, circumvention and measurability. In the present work we have proposed an EEG based human authentication system on a large dataset containing 104 healthy subjects; an issue which has not been focused in the literature. The method has been examined in two situations, closed and opened eyes. By extracting Autoregressive (AR) coefficients as the feature set, selecting the features using a statistical based method, doing a two-step feature normalization and selecting reference sweeps, the proposed system achieved to 97.43±0.00% accuracy using only 10% of the data in the training step.

With the increase of communication and financial transaction through internet, on-line signature verification is an accepted biometric technology for access control and plays a significant role in authenticity and authorization in modernized society. Therefore, fast and precise algorithms for the signature verification are very attractive. The goal of this paper is modeling of velocity signal that pattern and properties is stable for persons. With using pole-zero models based on discrete cosine transform, precise method is proposed for modeling and then features is founded from strokes. With using linear, parzen window and support vector machine classifiers, the signature verification technique was tested with a large number of authentic and forgery signatures and has demonstrated the good potential of this technique. The signatures are collected from three different database include a proprietary database, the SVC2004 and the Sabanci University signature database benchmark databases. Experimental results based on Persian, SVC2004 and SUSIG databases show that our method achieves an equal error rate of 5.91%, 5.62% and 3.91% in the skilled forgeries, respectively.