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This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based<jats:italic>k<\/jats:italic>nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.<\/jats:p>","DOI":"10.1155\/2015\/360217","type":"journal-article","created":{"date-parts":[[2015,5,31]],"date-time":"2015-05-31T21:02:36Z","timestamp":1433106156000},"page":"1-17","source":"Crossref","is-referenced-by-count":13,"title":["A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier"],"prefix":"10.1155","volume":"2015","author":[{"given":"Haryati","family":"Jaafar","sequence":"first","affiliation":[{"name":"Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salwani","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Intelligent Biometric Group, School of Electrical and Electronic Engineering, 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