A novel local pattern descriptor - Local vector pattern in high-order derivative space for face recognition

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Title:	A novel local pattern descriptor - Local vector pattern in high-order derivative space for face recognition
Authors:	范國清;Fan, Kuo-Chin;Hung, Tsung-Yung
Contributors:	資訊電機學院資訊工程學系
Keywords:	Applied sciences;Encoding;Exact sciences and technology;Face;Face recognition;Feature extraction;Image coding;Image processing;Information, signal and communications theory;Pattern recognition;Signal processing;Telecommunications and information theory;Transforms;Vectors
Date:	2014-01-01
Issue Date:	2026-04-23 13:08:12 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers Inc.;New York, NY: IEEE
Abstract:	摘要： In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local derivative directions from the \(n\) th-order LVP in \((n-1)\) th-order derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition. 其他題名： TIP 其他題名： IEEE Trans Image Process 出版者： New York, NY: IEEE 出版日期： 2014-07-01 出處： IEEE transactions on image processing, 2014-07, Vol.23 (7), p.2877-2891 資源來源： IEEE All-Society Periodicals Package (ASPP) 1998–Present 版權： 2015 INIST-CNRS 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jul 2014 識別號： ISSN: 1057-7149 識別號： ISSN: 1941-0042 識別號： EISSN: 1941-0042 識別號： DOI: 10.1109/TIP.2014.2321495 識別號： PMID: 24808409 識別號： CODEN: IIPRE4
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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