Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy under Ideal vs. Non-Ideal Conditions <s> This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent fingerprint verification competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future. <s> BIB001 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy under Ideal vs. Non-Ideal Conditions <s> In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures. <s> BIB002 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy under Ideal vs. Non-Ideal Conditions <s> Latent fingerprint images are typically obtained under non-ideal acquisition conditions, resulting in incomplete or distorted impression of a finger, and ridge structure corrupted by background noise. This necessitates involving latent experts in latent fingerprint examination, including assessing the value of a latent print as forensic evidence. However, it is now generally agreed that human factors (e.g., human visual perception, expertise of latent examiners, workload, etc.) can significantly affect the reliability and consistency of the value determinations made by latent examiners. We propose an objective quality measure for latent fingerprints, called Latent Fingerprint Image Quality (LFIQ), that can be effectively used to distinguish latent fingerprints of good quality, which do not require any human intervention, and to compensate for the subjective nature of value determination by latent examiners. We investigate several factors that determine the latent quality: (i) ridge quality based on ridge clarity and connectivity of good ridge structures, (ii) minutiae reliability based on a minutiae dictionary learnt from high quality minutia patches, and (iii) position of the finger by detecting a reference point. The proposed LFIQ metric is based on triangulation of minutiae incorporating the above three factors. Experimental results show that (i) the proposed LFIQ is a good predictor of the latent matching performance by AFIS and (ii) it is also correlated with value determination by latent examiners. <s> BIB003 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy under Ideal vs. Non-Ideal Conditions <s> Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate (“lights-out” capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving “lights-out” latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher. <s> BIB004 </s> Security and Accuracy of Fingerprint-Based Biometrics: A Review <s> Accuracy under Ideal vs. Non-Ideal Conditions <s> Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top $K$ candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars. <s> BIB005
Biometric systems sometimes confront unrealistic expectations of achieving the matching accuracy of traditional password-based authentication systems. A password-based system always offers a crisp result-it grants access if the input password is a match, and vice versa. However, biometric matching cannot be 100% accurate. The accuracy of a biometric system can be evaluated by using well-known performance indicators, e.g., False Accept Rate (FAR), False Reject Rate (FRR), and Equal Error Rate (EER). Recognition accuracy generally depends on factors such as input image quality and matching algorithms. With decades of efforts from researchers, remarkable matching accuracy has been achieved and reported. For instance, there is an online evaluation platform, named FVC-ongoing  , where researchers can upload their recognition algorithms and compete with other algorithms on matching accuracy. FVC-ongoing sets up a benchmark to evaluate those algorithms using a set of sequestered databases and the results are evaluated by indicators-FAR, FRR, and EER-which is the rate at which both acceptance and rejection errors are equal BIB001 . According to the latest results shown on the FVC-ongoing platform  , the best matching accuracy out of the fingerprint verification competition reached the EER = 0.022%, achieved by the algorithm, named HXKJ, contributed by Beijing Hisign Bio-info Institute. The best three matching results of fingerprint (with and without template protection) verification competitions are listed in Table 5 , extracted from  , from which we can see that all of them were contributed by companies. Some algorithms designed by academic researchers also achieve gratifying accuracy. The state-of-the-art MCC (Minutia Cylinder Code) BIB002 based fingerprint matching algorithm achieved the EER = 0.49% on database FVC2002 DB2, and the EER = 0.12% on database FVC2006 DB2  . However, all the above matching results are based on databases (e.g., FV-STD-1.0) containing images with better quality than images acquired under non-ideal conditions, such as fingerprint images from crime scenes. In Figure 3 , we show a comparison of sample fingerprint images from FV-STD-1.0 used in the FVC-ongoing Competition and sample images obtained from non-ideal conditions. The images acquired from the surfaces of objects at crime scenes are usually referred to as latent fingerprints, as shown in Figure 3 . Although state-of-the-art algorithms have achieved impressively high matching accuracy using rolled and plain images acquired in an attended mode, as shown in Table 5 , matching accuracy with latent fingerprints is still far from satisfactory due to factors like poor ridge structure, complex background noise, and non-linear distortion on the latent images BIB003 . Significant progress has been made in improving the matching accuracy of latent fingerprints. Cao et al. BIB004 proposed a latent segmentation and enhancement algorithm to refine a poor fingerprint image. By using a total variation decomposition model, the piecewise-smooth background noise can be removed and several overlapping patches are defined and used for latent enhancement, leading to better matching performance. Also, Araro et al. BIB005 incorporated feedback information from an exemplar to refine the extracted features from a latent fingerprint image with the eventual goal of increasing the matching accuracy. al. BIB004 proposed a latent segmentation and enhancement algorithm to refine a poor fingerprint image. By using a total variation decomposition model, the piecewise-smooth background noise can be removed and several overlapping patches are defined and used for latent enhancement, leading to better matching performance. Also, Araro et al. BIB005 incorporated feedback information from an exemplar to refine the extracted features from a latent fingerprint image with the eventual goal of increasing the matching accuracy. BIB004 ).