Konstantinos Sirlantzis

Biosignals for User Authentication - Towards Cognitive Biometrics?

Cognitive biometrics refers to a novel approach for user authentication/identification utilising biosignals which reflect the mental and emotional states of an individual. Specifically, current implementations rely on the use of the electroencephalogram (EEG), electrocardiogram (ECG), and the electro dermal response (EDR) as inputs into a traditional authentication scheme. The motivation for the deployment of biosignals resides in their potential uniqueness, universality, and their resistance to spoofing. The challenge with respect to cognitive biometrics based on biosignals is to enhance the information content of the acquired data. This paper presents a brief survey of the use of such biosignals to produce cognitive biometric systems for person recognition. The types of signals used and their claimed effectiveness is presented and compared. The paper concludes with a description of the challenges facing the deployment of cognitive biometrics, including sensor design issues and the need to extract information-rich and robust features.

A new chain-code quantization approach enabling high performance handwriting recognition based on multi-classi .er schemes

In this paper initially we propose a novel approach toclassify handwritten characters based on a directional decompositionof the corresponding chain-code representation.This is alternative to previous transformations of thechain-codes proposed by the authors, namely the orderedand random decomposition of the bit-planes resulting fromthe binary representation of the chain-codes. Subsequentlywe utilize the power of the recently developed multiple classifierschemes using sntuple classifiers to integrate the complimentaryinformation encapsulated in all three transformationsinto a more powerful and robust character recognitionsystem. The results obtained through a series ofcross-validation experiments show that the proposed fusionscheme not only outperforms its constituent parts and anumber of other successful classifiers, but also enables significantsavings in memory requirements compared to theoriginal sntuple-based recognition system.

Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition

We describe a multiple classifier system which incorporates an automatic self-configuration scheme based on genetic algorithms. Our main interest in this paper is focused on exploring the statistical properties of the resulting multi-expert configurations. To this end we initially test the proposed system on a series of tasks of increasing difficulty drawn from the domain of character recognition. We then proceed to investigate the performance of our system not only in comparison to that of its constituent classifiers, but also in comparison to an independent set of individually optimised classifiers. Our results illustrate that significant gains can be obtained by integrating a genetic algorithm based optimisation process into multi-classifier schemes both in the performance enhancement and in the reduction of its volatility, especially as the task domain becomes more complex.

Trainable Multiple Classifier Schemes for Handwritten Character Recognition

In this paper we propose two novel multiple classifier fusion schemes which, although different in terms of architecture, share the idea of dynamically extracting additional statistical information about the individually trained participant classifiers by reinterpreting their outputs on a validation set. This is achieved through training on the resulting intermediate feature spaces of another classifier, be it a combiner or an intermediate stage classification device. We subsequently implemented our proposals as multi-classifier systems for handwritten character recognition and compare the performance obtained through a series of cross-validation experiments of increasing difficulty. Our findings strongly suggest that both schemes can successfully overcome the limitations imposed on fixed combination strategies from the requirement of comparable performance levels among their participant classifiers. In addition, the results presented demonstrate the significant gains achieved by our proposals in comparison withb oth individual classifiers experimentally optimized for the task in hand, and a multi-classifier system design process which incorporates artificial intelligence techniques.

Improving colour iris segmentation using a model selection technique

Analysis of circle and ellipse based iris segmentation models.A novel model selection method to improve colour iris segmentation.Showing the effectiveness of HOG feature for model selection.Analysis of the experimental results on both mobile and static camera data. In this paper, we propose a novel method to improve the reliability and accuracy of colour iris segmentation for captures both from static and mobile devices. Our method is a fusion strategy based on selection among the segmentation outcomes of different segmentation methods or models. First, we present and analyse an iris segmentation framework which uses three different models to show that improvements can be obtained by selection among the outcomes generated by the three models. Then, we introduce a model selection method which defines the optimal segmentation based on a ring-shaped region around the outer segmentation boundary identified by each model. We use the histogram of oriented gradients (HOG) as features extracted from the ring-shaped region, and train a SVM-based classifier which provides the selection decision. Experiments on colour iris datasets, captured by mobile devices and static camera, show that the proposed method achieves an improved performance compared to the individual iris segmentation models and existing algorithms.

Are Two Eyes Better than One? An Experimental Investigation on Dual Iris Recognition

Iris recognition using both eyes of an individual has not been extensively researched in the available literature. However, for iris recognition at a distance, capturing a good quality image of the same eye every time is a challenging task and a dual iris approach is potentially beneficial. In the present work, we investigate the advantages of using dual iris approach for iris recognition, comparing the performances of using both eyes with those obtained using only one eye.

Colour space fusion for texture recognition

In this paper we propose a novel approach to colour texture classification based on fusion of the information contained in different colour spaces. In colour texture classification the choice of the most effective colour space to use is still an open issue. However, combining the strengths of different colour spaces may offer an alternative solution to the problem of robust texture discrimination. The principal aim of the work presented here is to study the performance of such decision combination approaches using classifiers obtained through training on features extracted from a number of colour space and subspace representations of the same texture classes. To this end we performed a number of cross-validation experiments involving six different colour spaces and their chromatic subspaces. Our results strongly suggest that colour texture classification can benefit significantly from techniques based on multiple classifier combination strategies.

Bit plane decomposition and the scanning n-tuple classifier

This paper describes a multiple classifier configuration for high performance off-line handwritten character recognition applications. Along with a conventional scanning n-tuple classifier (or sn-tuple) implementation, three other sn-tuple systems have been used which are trained using a binary feature set extracted from the contour chain-codes using a novel decomposition technique. The overall accuracy thus achievable by the proposed scheme is much higher than most other classification systems available and the added complexity (over conventional sn-tuple system) is minimal.

Iris liveness detection using regional features

Regional features with both low level features and high level feature distribution.Intensity and local descriptors as low level features.Spatial pyramid model seeking feature distribution in regions with varying size.Relational measure expressing feature distribution in regions with varying shape.Experiments on both NIR and colour datasets. In this paper, we exploit regional features for iris liveness detection. Regional features are designed based on the relationship of the features in neighbouring regions. They essentially capture the feature distribution among neighbouring regions. We construct the regional features via two models: spatial pyramid and relational measure which seek the feature distributions in regions with varying size and shape respectively. The spatial pyramid model extracts features from coarse to fine grid regions, and, it models a local to global feature distribution. The local distribution captures the local feature variations while the global distribution includes the information that is more robust to translational transform. The relational measure is based on a feature-level convolution operation defined in this paper. By varying the shape of the convolution kernel, we are able to obtain the feature distribution in regions with different shapes. To combine the feature distribution information in regions with varying size and shape, we fuse the results based on the two models at the score level. Experimental results on benchmark datasets demonstrate that the proposed method achieves an improved performance compared to state-of-the-art features.

Investigation of a novel self-configurable multiple classifier system for character recognition

In this paper we introduce a global optimisation technique, namely a genetic algorithm, into a parallel multiclassifier system design process. As few similar systems have been proposed to date our main focus in this study is to explore the statistical properties of the self-configuration process in order to enhance our understanding of its internal operational mechanism and to propose possible improvements. For this we tested our system in a series of character recognition tasks ranging from printed to handwritten data. Subsequently, we compare its performance with that of two alternative multiple classifier combination strategies. Finally, we investigate, over a set of cross-validating experiments, the relation between the performances of the individual classifiers and their variability, and the frequency with which each of them is chosen to participate in the final configuration generated by the genetic algorithm.