Chaotic Neural Network and Multidimensional Data Analysis in Biometric Applications
In this book chapter, a novel biometric system from the normalisation level up to the verification level is developed, tested and verified against other multimodal and unimodal systems. The main advantage of a new architecture is in flexibility of combining various features from multimodal biometrics in a new way, suitable for neural-network learner. The system utilises associative memories and pattern matchers as learners of biometric data, but the main advantage of a new architecture is increased resistance to noise and ability of system to compensate for an absence of some biometric traits. Detailed experimental analysis of pros and cons of such system is also provided.
KeywordsSubspace Cluster Biometric Data Biometric System False Acceptance Rate False Rejection Rate
Authors acknowledge invaluable help and advice for Prof Khalid Saeed while working on this chapter. This research was partially supported by NSERC.
- 2.Ross A, Jain A (2004) Multimodal biometrics: an overview. In: Proceedings of 12th European signal processing conference (EUSIPCO), Vienna, Austria, pp 1221–1224Google Scholar
- 5.Ahmadian K, Gavrilova M (2011) A novel multi-modal biometric architecture for high-dimensional features. In: Cyberworlds, Banff, Canada, IEEE proceedings, pp 9–16Google Scholar
- 6.Yambor W (2000) Analysis of PCA-based and fisher discriminant-based image recognition. Technical report. Computer Science Department, Colorado State UniversityGoogle Scholar
- 7.Gavrilova M, Ahmadian K (2011) Dealing with biometric multi-dimensionality through novel chaotic neural network methodology in issue on advances and trends in biometrics. Int J Inf Technol Manage 11(1/2):18–34Google Scholar