Recent Advances in Biometric Systems: A Signal Processing Perspective

Natalia A Schmid, Stephanie Schuckers, Jonathon Phillips, Kevin Bowyer
<span title="2010-02-16">2010</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="" style="color: black;">EURASIP Journal on Advances in Signal Processing</a> </i> &nbsp;
We were pleased to receive a total of thirty-nine submissions to the special issue on "Recent advances in biometric systems: a signal processing perspective." The Guest Editors divided up the responsibility for the submissions, and each submission was reviewed by a minimum of two experts in the relevant area of biometrics. Following the first round of reviews, some of the submissions were revised by the authors and then underwent a second round of review. The final result of the process is the
more &raquo; ... et of fifteen papers that appear in this special issue. The first six papers all deal with face recognition in some respect. Then we have one paper dealing with iris biometrics and one dealing with recognition by gait. The topic of the next two papers is fingerprint image analysis and the following paper addresses the related topic of palmprint analysis. The next two papers cover issues in signature verification. Lastly, there is one paper on retinal verification and one on using electrocardiogram signals as a biometric. The broad variety of topics in this special issue represents the dynamism and breadth of biometrics. In "Recognition of faces in unconstrained environments: a comparative study," Ruiz-del-Solar, Verschae and Correa present the results of a comparative study of existing face recognition methods in the context of unconstrained environments. The recognition approaches considered include two local-matching methods, histograms of LBP features and Gabor Jet descriptors, one holistic method, generalized PCA, and two novel image-matching methods, SIFT-based and ERCF-based. The FERET, LFW, UCHFaceHRI, and FRGC face databases are used in the evaluation. Two conclusions are that there is a large dependence of the methods on the amount of face and background information in the image, and that outdoor illumination results in a large decrease in the performance of all of the methods. In "Facial expression biometrics using statistical shape models," Shark et al. perform face recognition by combining 3D range images and expression. The authors' method is based on a shape space vector derived from a statistical shape model for 3D range data. Experimental results are reported on the SUNY Binghamton BU-3DFE dataset of the 3D face images. Results are reported for both recognition and expression classification. In "Evolutionary discriminant feature extraction with application to face recognition," Lu et al. present a technique that searches for subspaces to represent faces. The search technique is based on evolutionary computing and is designed to be efficient. One reason the algorithm is efficient is because the search space is confined to discriminatory subspaces. In "Comparison of spectral-only and spectral/spatial face recognition for personal identity verification," Pan et al. compare the performance of single-band, multiband, and combined spectral/spatial approaches to face recognition. They use the eigenface algorithm from the CSU Face Identification Evaluation System for the basic recognition engine. Multiband eigenface methods in which the multiple bands are processed independently are shown to improve face recognition performance relative to single-band results.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1155/2009/128752</a> <a target="_blank" rel="external noopener" href="">fatcat:k2tlxamk6fdxfaikrwuxtwsfgm</a> </span>
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