In practical biometric verification applications, we expect to observe a large variability of biometric data and single classifiers may not be very accurate. In such cases, fusion of multiple classifiers may improve accuracy. Statistical dependence of classifiers has recently been shown to improve accuracy over statistically independent classifiers. In this paper, we focus on the verification application and theoretically analyze the OR fusion rule to find the favorable and unfavorable conditional dependence between classifiers. Favorably dependent correlation filter based classifiers for the OR rule are designed on the fingerprint NIST 24 plastic distortion and rotation datasets. For the plastic distortion dataset, unconstrained optimal tradeoff (UOTF) correlation filters were used because of their distortion tolerance and discrimination capability; and for the rotation dataset, optimal trade-off circular harmonic function (OTCHF) filters were used because of their tolerance to geometric rotation. On the plastic distortion dataset, three favorably dependent classifiers were designed on different distortions of the finger, each with an EER of 15.7%, 14.3%, and 9.8%
respectively. The OR fusion of these three classifiers has an Equal Error Rate (EER) of 1.8% while the best single UOTF based classifier has an EER of 2.8%. On the rotation dataset, five OTCHF filter based classifiers were designed for tolerance to different rotation angle ranges of a finger with an average individual EER of 38.8%. The OR rule fusion has an EER of 14.6%; whereas the best single OTCHF filter has an EER of 27.7%. It is also shown that the best fusion rule is the OR rule for these classifiers that were designed to be favorable for the OR rule.
Reliable verification and identification can be achieved by fusing hard and soft information from multiple classifiers. Correlation filter based classifiers have shown good performance in biometric verification applications. In this paper, we develop a method of fusing soft information from multiple correlation filters. Usually, correlation filters are designed to produce a strong peak in the correlation filter output for authentics whereas no such peak should be produced for impostors. Traditionally, the peak-to-sidelobe-ratio (PSR) has been used to characterize the strength of the peak and thresholds are set on the PSR in order to determine whether the test image is an authentic or an impostor. In this paper, we propose to fuse multiple correlation output planes, by appending them for classification by a Support Vector Machine (SVM), to improve the performance over traditional PSR based classification. Multiple Unconstrained Optimal Tradeoff Synthetic Discriminant Function (UOTSDF) filters having varying degrees of discrimination and distortion tolerance are employed here to create a feature vector for classification by a SVM, and this idea is evaluated on the plastic distortion set of the NIST 24 fingerprint database. Results on this database provide an Equal Error Rate (EER) of 1.36% when we fuse correlation planes, in comparison to an average EER of 3.24% using the traditional PSR based classification from a filter, and 2.4% EER on fusion of PSR scores from the same filters using SVM, which demonstrates the advantages of fusing the correlation output planes over the fusion of just the peak-to-sidelobe-ratios (PSRs).
Face authentication involves capturing the face images and representing the image suitable for matching with the reference template. In this paper, we discuss a new representation for matching that involves processing the image using 1-D processing that offers potential speed improvements over more conventional 2-D processing methods. Although the test application that is being considered here is to access a computer using face verification, this method can be used in other face verification applications. In this application, the subject is assumed to be cooperative, and the environment for capturing the face images is somewhat controlled. The proposed 1-D processing helps to locate the eyes, which in turn helps to normalize the face image for representation and matching. 1-D eigenanalysis is performed on the normalized face image to derive the eigenvectors. The face image is represented using components projected onto these eigenvectors. The 1-D PCA provides advantages over the conventional 2-D PCA in terms of providing a better model of the face in practical situations and providing robustness to local changes in the authentic images. We show that matching a test image with a reference image using the eigencomponents improves the discrimination between genuine and impostor face images. Our studies show good performance and it seems possible to obtain in practice an equal error rate (EER) close to zero.
Biometric authentication can provide an added level of security and/or ease of convenience in access control applications. Fingerprints are a popular choice among the biometric features and have been successfully used in criminal identification. In access control applications, we are interested in obtaining digital live-scan fingerprints from sensors, rather than the inked fingerprints usually used in criminal identification. In this paper, we evaluate the performance of composite correlation filters in fingerprint verification for access control applications. The NIST Special Database 24, obtained from an optical fingerprint sensor, is used to evaluate the performance of fingerprint verification in the presence of distortion.
Biometric verification refers to the process of matching an input biometric to stored biometric information. In particular, biometric verification refers to matching the live biometric input from an individual to the stored biometric template of that individual. Examples of biometrics include face images, fingerprint images, iris images, retinal scans, etc. Thus, image processing techniques prove useful in biometric recognition. In particular, composite correlation filters have proven to be effective. In this paper, we will discuss the application of composite correlation filters to biometric verification.
Composite correlation filters (also known as synthetic discriminant function or SDF filters) are attractive for automatic target recognition (ATR) due to their built-in shift-invariance and their potential for trading off distortion tolerance for discrimination. Although the recognition performances of many advanced correlation filters are attractive, their computational complexities can be daunting particularly for ATR applications where the target detection and identification must be achieved in limited time. In this paper, we discuss some methods to reduce the complexity of correlation filter design, performing the cross-correlation as well as the processing of the resulting correlation outputs.
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