Cascaded Filtering of Biometric Identification Using Random Projection

Atif Iqbal (homepage)

Biometrics has a long history, and is possibly as old as the human race itself. It is often used as an advanced security measure to safeguard important artifacts, buildings, information, etc. Biometrics is increasingly being used for secure authentication of individuals and is making its presence felt in our lives. With the fast increase in computational power, such systems can now be deployed on a very large scale. However, efficiency in large scale biometric matching is still a concern as the problem of deduplication (removal of duplicates) of a biometric database with N entries is O(N²), which can be extremely challenging for large databases. To make this problem tractable, many indexing methods have been proposed that would speed up the comparison process. However, the expectation of accuracy in such systems combined with the nature of biometric data makes the problem a very challenging one.

Biometric identification often involves explicit comparison of a probe template against each template stored in a database. An effective approach to speed up the process is that of filtering, where a lightweight comparison is used to reduce the database to smaller set of candidates for explicit comparison. However, most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work, we show that a cascade of simple linear projections on random lines can achieve significant levels of filtering. Each stage of filtering consists of projecting the probe onto a specific line and removal of database samples outside a window around the probe. The approach provides a way of automatic generation of filters and avoids the need of developing specific features for different biometric traits. The method also provides us with a variety of parameters such as the projection lines, the number and order of projections, and the window sizes to customize the filtering process to a specific application. The experiments are performed on the fingerprints, palmprints and iris.

For both iris and palmprint datasets, the representation that we use (before projection) is the popularly used thresholded filter response from pre-defined regions of the image. Experimental results show that using an ensemble of projections reduce the search space by 60% without increasing the false negative identification rate in palmprint. However for stronger biometrics such as iris, the approach does not yield similar results. We further explore this problem to find a solution, specifically for the case of fingerprints.

The fundamental approach here is to explore the effectiveness of weak features in a cascade for filtering fingerprint databases. We start with a set of potential indexing features computed from minutiae triplets and minutiae quadruplets. We show that by using a set of random lines and the proposed fitness function, one can achieve better results that optimized projection methods such as PCA or LDA. Experimental results on fingerprint datasets show that using an ensemble of projections we can reduce the penetration to 26% at a hit rate of 99%. As each stage of the cascade is extremely fast, and filtering is progressive along the cascade, one can terminate the cascade at any point to achieve the desired performance. One can also combine this method with other indexing methods to improve the overall accuracy and speed. We present detailed experimental results on various aspects of the process on the FVC 2002 dataset.

The proposed approach is scalable to large datasets due to the use of random linear projections and direcly lends to pipelined processing. The method also allows the use of multiple existing features without affecting the computation time.

Related Publications

• Atif Iqbal and Anoop M. Namboodiri - Cascaded Filtering for Biometric Identification using Random Projections Proceedings of Seventh National Conference on Communications (NCC 2011),28-30 Jan, 2011, Bangalore, India. [PDF]

• Atif Iqbal and Anoop M. Nambodiri - Cascaded Filtering for Fingerprint Identification using Random Projection, IEEE Compter Society Conference Computer Vision and Pattern Recognition Workshops, June 2012

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