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A Scheme for Template Security at Feature Fusion Level in Multimodal Biometric System

Selwal A., Gupta S. K., Kumar S.

Advances in Science and Technology. Research Journal

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2016

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Vol. 10, nr 31

23--30

EN

Biometrics is the science of human recognition by means of their biological, chemical or behavioural traits. These systems are used in many real life applications simply from biometric based attendance system to providing security at a very sophisticated level. A biometric system deals with raw data captured using a sensor and feature template extracted from raw image. One of the challenges being faced by designers of these systems is to secure template data extracted from the biometric modalities of the user and protect the raw images. In order to minimize spoof attacks on biometric systems by unauthorised users one of the solutions is to use multi-biometric systems. Multi-modal biometric system works by using fusion technique to merge feature templates generated from different modalities of the human. In this work, a novel scheme is proposed to secure template during feature fusion level. The scheme is based on union operation of fuzzy relations of templates of modalities during fusion process of multimodal biometric systems. This approach serves dual purpose of feature fusion as well as transformation of templates into a single secured non invertible template. The proposed technique is irreversible, diverse and experimentally tested on a bimodal biometric system comprising of fingerprint and hand geometry. The given scheme results into significant improvement in the performance of the system with lower equal error rate and improvement in genuine acceptance rate.

2

Music Recommendation System

Hoffmann P., Kaczmarek A., Spaleniak P., Kostek B.

Journal of Telecommunications and Information Technology

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2014

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nr 2

59--69

EN

The paper focuses on optimization vector content feature for the music recommendation system. For the purpose of experiments a database is created consisting of excerpts of music files. They are assigned to 22 classes corresponding to different music genres. Various feature vectors based on low-level signal descriptors are tested and then optimized using correlation analysis and Principal Component Analysis (PCA). Results of the experiments are shown for the variety of feature vectors. Also, a music recommendation system is presented along with its main user interfaces.

3

Investigation of normalization techniques and their impact on a recognition rate in handwritten numeral recognition

Chmielnicki W., Stąpor K.

Schedae Informaticae

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2010

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Vol. 19

53-77

EN

This paper presents several normalization techniques used in handwritten numeral recognition and their impact on recognition rates. Experiments with five different feature vectors based on geometric invariants, Zernike moments and gradient features are conducted. The recognition rates obtained using combination of these methods with gradient features and the SVM-rbf classifier are comparable to the best state-of-art techniques.

4

Independent component analysis in angiography images

Snitkowska E., Kasprzak W.

Archives of Control Sciences

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2006

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Vol. 16, no. 3

283-296

EN

An important source for information about digital content is the texture of image regions. This paper presents a feature extraction approach that is based on independent component analysis (ICA). In ICA a transformation of measured vectored time series is discovered via blind signal processing that gives statistically independent source signals. In our approach every textured region is considered as a mixture of statistically independent source regions, scanned to 1-D time series. After these sources, called independent components, are extracted by ICA, optimally for given image type, the mixing coefficients of particular region constitute its feature vector. The quality of such features is experimentally verified and compared to other common feature schemas. The comparison procedure explores the Fisher information criterion and classification results for feature evaluation. Our application field is the analysis of angiography images. It is difficult for medical doctors properly to classify such images, hence an automatic tool could provide support in this matter. We demonstrate the usefulness of ICA-based features for automatic evaluation of angiography images.