{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:14:00Z","timestamp":1760955240433,"version":"3.41.2"},"reference-count":30,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2010,11,23]],"date-time":"2010-11-23T00:00:00Z","timestamp":1290470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,11,23]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to propose an effective method to perform off\u2010line signature verification and identification by applying a local shape descriptor pyramid histogram of oriented gradients (PHOGs), which represents local shape of an image by a histogram of edge orientations computed for each image sub\u2010region, quantized into a number of bins. Each bin in the PHOG histogram represents the number of edges that have orientations within a certain angular range.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>Automatic signature verification and identification are then studied in the general binary and multi\u2010class pattern classification framework, with five different common applied classifiers thoroughly compared.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>Simulation experiments show that PHOG has obvious advantages in the extraction of discriminating information from handwriting signature images compared with many previously proposed signature feature extraction approaches. The experiments also demonstrate that several classifiers, including<jats:italic>k<\/jats:italic>\u2010nearest neighbour, multiple layer perceptron and support vector machine (SVM) can all give very satisfactory performance with regard to false acceptance rate (FAR) and false rejection rate (FRR). On a public benchmarking signature database \u201cGrupo de Procesado Digital de Senales\u201d (GPDS), experiments demonstrate an FRR of 4.0 percent and an FAR 3.25 percent from SVM for skillful forgery, which compares sharply with the latest published results of FRR 16.4 percent and FAR 14.2 percent on the same dataset. Experiments on a second DAVAB off\u2010line signature database also illustrate the superiority of the proposed method. The related issue, off\u2010line signature recognition, which is to find the identification of the signature owner from a given signature database, is also investigated based on the PHOG features, showing superb classification accuracies of 99 and 96 percent for GPDS and DAVAB datasets, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The proposed method for off\u2010line signature verification and recognition has a promising potential of designing a real\u2010world system for many applications, particularly in forensics and biometrics.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17563781011094197","type":"journal-article","created":{"date-parts":[[2010,11,27]],"date-time":"2010-11-27T07:06:09Z","timestamp":1290841569000},"page":"611-630","source":"Crossref","is-referenced-by-count":29,"title":["Off\u2010line signature verification and identification by pyramid histogram of oriented gradients"],"prefix":"10.1108","volume":"3","author":[{"given":"Bailing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2022020619550013700_b9","doi-asserted-by":"crossref","unstructured":"Baltzakis, H. and Papamarkos, N. 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