Computational workload in biometric identification systems: an overview <s> Dimensionality reduction: <s> Biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information. <s> BIB001 </s> Computational workload in biometric identification systems: an overview <s> Dimensionality reduction: <s> Minutiae, which are the endpoints and bifurcations of fingerprint ridges, allow a very discriminative classification of fingerprints. However, a minutiae set is an unordered set and the minutiae locations suffer from various deformations such as translation, rotation and scaling. In this paper, we introduce a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. By applying the spectral minutiae representation, we can combine the fingerprint recognition system with a template protection scheme, which requires a fixed-length feature vector. This paper also presents two spectral minutiae matching algorithms and shows experimental results. <s> BIB002 </s> Computational workload in biometric identification systems: an overview <s> Dimensionality reduction: <s> In this paper, an enhanced image-based fingerprint verification algorithm is proposed to improve matching accuracy and processing speed by overcoming the demerits of previous methods over poor-quality images. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point, and then aligns the image according to the position and orientation of reference point to avoid time-consuming alignment. A set of fixed-length moment features, invariant to the affine transform, is extracted from tessellated cells on a region of interest (ROI) centered at the reference point. The similarity between an input and a template in a database is evaluated by eigenvalue-weighted cosine (EWC) distance. Experimental results show that the proposed method has better performance in accuracy and speed comparing with other renowned methods. <s> BIB003 </s> Computational workload in biometric identification systems: an overview <s> Dimensionality reduction: <s> The texture in a human iris has been shown to have good individual distinctiveness and thus is suitable for use in reliable identification. A conventional iris recognition system unwraps the iris image and generates a binary feature vector by quantizing the response of selected filters applied to the rows of this image. Typically there are 360 angular sectors, 64 radial rings, and 2 filter responses. This produces a full-length iris code (FLIC) of about 5760 bytes. In contrast, this paper seeks to shrink the representation by finding those regions of the iris that contain the most descriptive potential. We show through experiments that the regions close to the pupil and sclera contribute least to discrimination, and that there is a high correlation between adjacent radial rings. Using these observations we produce a short-length iris code (SLIC) of only 450 bytes. The SLIC is an order of magnitude smaller the FLIC and yet has comparable performance as shown by results on the MMU2 database. The smaller sized representation has the advantage of being easier to store as a barcode, and also reduces the matching time per pair. <s> BIB004 </s> Computational workload in biometric identification systems: an overview <s> Dimensionality reduction: <s> This paper describes a generic fusion technique for iris recognition at bit-level we refer to as Selective Bits Fusion. Instead of storing multiple biometric templates for each algorithm, the proposed approach extracts most discriminative bits from multiple algorithms into a new template being even smaller than templates for individual algorithms. Experiments for three individual iris recognition algorithms on the open CASIA-V3-Interval iris database illustrate the ability of this technique to improve accuracy and processing time simultaneously. In all tested configurations Selective Bits Fusion turned out to be more accurate than fusion using the Sum Rule while being about twice as fast. The design of the new template allows explicit control of processing time requirements and introduces a tradeoff between time and accuracy of biometric fusion, which is highlighted in this work. <s> BIB005
Templates produced through dimensionality reduction can be used directly as a replacement for the full-sized templates (e.g. through PCA). Additionally, they can serve as a first pre-filtering step in a two-stage system (see subsection 3.1 for examples). An illustrative example is BIB004 (and a similar approach in BIB005 ), where the so-called "short-length Iris-Codes', which comprise the most discriminative parts of the normal Iris-Codes, are presented. The transformed templates are an order of magnitude smaller than the original ones, and exhibit somewhat impaired biometric performance when benchmarked against the original templates, thereby making them good candidates for a pre-filtering step. In biometrics, most prominently used variable-sized feature representation is that of fingerprint minutiae. The number of minutiae points can be inherently different between different data subjects and can further be augmented depending on the sample acquisition conditions (i.e. the so-called missing and spurious minutiae). In the literature, a number of alternative approaches to the traditional minutiae-based fingerprint comparison algorithm has been proposed by several authors BIB001 BIB002 BIB003 . All of those methods achieve biometric performance and computational workload results competitive with those of the traditional variable-size, minutiae-based algorithm.