Learning framework for examiner-centric fingerprint classification using spectral features

Paul W. H. Kwan; Yi Guo; Junbin Gao

doi:10.1117/12.749777

15 November 2007 Learning framework for examiner-centric fingerprint classification using spectral features

Paul W. H. Kwan, Yi Guo, Junbin Gao

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881H (2007) https://doi.org/10.1117/12.749777
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

In recent years, the tasks of fingerprint examiners have been greatly aided by the development of automatic fingerprint classification systems. These systems operate by matching low-level features automatically extracted from fingerprint images, often represented collectively as numeric vectors, for their decision. However, there are two major shortcomings in current systems. First, the result of classification depends solely on the chosen features and the algorithm that matches them. Second, the systems cannot adapt their results over time through interaction with individual fingerprint examiners who often have different degrees of experiences. In this paper, we demonstrate by incorporating relevance feedback in a fingerprint classification system, a personalized semantic space over the database of fingerprints for each user can be incrementally learned. The fingerprint features that induce the initial features space from which individual semantic spaces are being learned were obtained by multispectral decomposition of fingerprints using a bank of Gabor filters. In this learning framework, the out-of-sample extension of a recently introduced dimensionality reduction method, called Twin Kernel Embedding (TKE), is applied to learn both the semantic space and a mapping function for classifying novel fingerprints. Experimental results confirm this learning framework for examiner-centric fingerprint classification.

Citation Download Citation

Paul W. H. Kwan, Yi Guo, and Junbin Gao "Learning framework for examiner-centric fingerprint classification using spectral features", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881H (15 November 2007); https://doi.org/10.1117/12.749777

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KEYWORDS

Classification systems

Image filtering

Databases

Feature extraction

Optical filters

Spatial frequencies

Image resolution

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