{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:34:05Z","timestamp":1777703645337,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2015,8,31]],"date-time":"2015-08-31T00:00:00Z","timestamp":1440979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,8,31]]},"abstract":"<jats:p>\n                    To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver\u2019s conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver\u2019s fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and cross-validation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the\n                    <jats:italic>k<\/jats:italic>\n                    -nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP. With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90% in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver\u2019s conditions towards the human-centric driver fatigue monitoring system.\n                  <\/jats:p>","DOI":"10.3233\/ifs-151779","type":"journal-article","created":{"date-parts":[[2016,1,15]],"date-time":"2016-01-15T12:25:57Z","timestamp":1452860757000},"page":"547-560","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Recognition of driver\u2019s fatigue expression using Local Multiresolution Derivative Pattern"],"prefix":"10.1177","volume":"30","author":[{"given":"Chihang","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Transportation, Southeast University, Nanjing, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Transportation, Southeast University, Nanjing, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering, Griffith University, Brisbane, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"He","sequence":"additional","affiliation":[{"name":"College of Transportation, Southeast University, Nanjing, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2015,10,6]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"The role of driver fatigue in commercial road transport crashes","author":"Croo H.D.","year":"2001","unstructured":"CrooH.D., BandmannM., MackayG.M., et al., The role of driver fatigue in commercial road transport crashes, Eur Transp Safety Council, Brussels, Belgium, Tech Rep, 2001.","journal-title":"Eur Transp Safety Council, Brussels, Belgium, Tech Rep"},{"key":"e_1_3_2_3_2","unstructured":"The Royal Society for the Prevention of Accidents. Driver fatigue and road accidents: A literature review and position. Birmingham UK 2001"},{"issue":"1","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TITS.2006.869598","article-title":"Real-time system for monitoring driver vigilance","volume":"7","author":"Bergasa L.","year":"2006","unstructured":"BergasaL., NuevoJ., SoteloM., et al., Real-time system for monitoring driver vigilance, IEEE Trans Intell Transp Syst 7(1) (2006), 63\u201377.","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"e_1_3_2_5_2","first-page":"2891","article-title":"Measurement of driver\u2019s consciousness by image processing\u2013A method for presuming driver\u2019s drowsiness by eye-blinks coping with individual differences","volume":"4","author":"Suzuki M.","year":"2006","unstructured":"SuzukiM., YamamotoN., YamamotoO., et al., Measurement of driver\u2019s consciousness by image processing\u2013A method for presuming driver\u2019s drowsiness by eye-blinks coping with individual differences, Proc IEEE Int Conf Syst, Man, Cybern 4 (2006), 2891\u20132896.","journal-title":"Proc IEEE Int Conf Syst, Man, Cybern"},{"key":"e_1_3_2_6_2","unstructured":"EskandarianA. SayedR. DelaigueP. et al. Advanced driver fatigue research U.S. Dept Transp Fed Motor Carrier Safety Admin Washington DC Tech Rep Rep 2007."},{"issue":"8","key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1016\/j.patcog.2007.01.018","article-title":"A visual approach for driver inattention detection","volume":"40","author":"Orazio T.D.","year":"2007","unstructured":"OrazioT.D., LeoM. and GuaragnellaC., A visual approach for driver inattention detection, Pattern Recognit 40(8) (2007), 2341\u20132355.","journal-title":"Pattern Recognit"},{"key":"e_1_3_2_8_2","unstructured":"BarrL. HowarathH. PopkinS. et al. A review and evaluation of emerging driver fatigue detection measures and technologies A Report of U.S. Department of Transportation Washington DC 2009."},{"key":"e_1_3_2_9_2","first-page":"101","article-title":"Camera-based drowsiness reference for driver state classification under real driving conditions","author":"Friedrichs F.","year":"2010","unstructured":"FriedrichsF. and YangB., Camera-based drowsiness reference for driver state classification under real driving conditions, Proc IEEE Intell Veh Symp (2010), 101\u2013106.","journal-title":"Proc IEEE Intell Veh Symp"},{"issue":"3","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.patrec.2009.08.014","article-title":"Gabor-based dynamic representation for human fatigue monitoring in facial image sequences","volume":"31","author":"Fan X.","year":"2010","unstructured":"FanX., SunY. and YinB., Gabor-based dynamic representation for human fatigue monitoring in facial image sequences, Pattern Recognit Lett 31(3) (2010), 234\u2013243.","journal-title":"Pattern Recognit Lett"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2092770"},{"issue":"12","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"2726","DOI":"10.1109\/TCSI.2005.857555","article-title":"EEG-based drowsiness estimation for safety driving using independent component analysis","volume":"52","author":"Lin C.T.","year":"2005","unstructured":"LinC.T., WuR.C., LiangS.F., et al., EEG-based drowsiness estimation for safety driving using independent component analysis, IEEE Trans Circuits Syst I, Reg Papers 52(12) (2005), 2726\u20132738.","journal-title":"IEEE Trans Circuits Syst I, Reg Papers"},{"issue":"5","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TBME.2008.918566","article-title":"Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver\u2019s drowsiness detection and warning","volume":"55","author":"Lin C.T.","year":"2008","unstructured":"LinC.T., ChenY.C., HuangT.Y., et al., Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver\u2019s drowsiness detection and warning, IEEE Trans Biomed Eng 55(5) (2008), 1582\u20131591.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TITS.2008.928241","article-title":"Fuzzy fusion of eyelid activity indicators for hypovigilance-related accident prediction","volume":"9","author":"Damousis I.G.","year":"2008","unstructured":"DamousisI.G. and TzovarasD., Fuzzy fusion of eyelid activity indicators for hypovigilance-related accident prediction, IEEE Trans Intell Transp Syst 9(3) (2008), 491\u2013500.","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2008.01.007"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.043"},{"issue":"4","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"7651","DOI":"10.1016\/j.eswa.2008.09.030","article-title":"Driver drowsiness detection with eyelid-related parameters by support vector machine","volume":"36","author":"Hu S.","year":"2009","unstructured":"HuS. and ZhengG., Driver drowsiness detection with eyelid-related parameters by support vector machine, Expert Syst Appl 36(4) (2009), 7651\u20137658.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.01.011"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2010.01.001"},{"issue":"5","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.trf.2010.06.006","article-title":"EEG signal analysis for the assessment and quantification of driver\u2019s fatigue","volume":"13","author":"Sibsambhu K.","year":"2010","unstructured":"SibsambhuK., MayankB. and AurobindaR., EEG signal analysis for the assessment and quantification of driver\u2019s fatigue, Transportation Research Part F Traffic Psychology and Behaviour 13(5) (2010), 297\u2013306.","journal-title":"Transportation Research Part F Traffic Psychology and Behaviour"},{"issue":"1","key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TBME.2010.2077291","article-title":"Driver drowsiness classification using fuzzy wavelet-packet-based feature extraction algorithm","volume":"58","author":"Khushaba R.N.","year":"2011","unstructured":"KhushabaR.N., KodagodaS., SaraL., et al., Driver drowsiness classification using fuzzy wavelet-packet-based feature extraction algorithm, IEEE Transactions on Biomedical Engineering 58(1) (2011), 121\u2013131.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/TIP.2009.2035882","article-title":"Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor","volume":"19","author":"Zhang B.","year":"2010","unstructured":"ZhangB., GaoS., ZhaoS., et al., Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor, IEEE Transactions on Image Processing 19(2) (2010), 533\u2013544.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"issue":"9","key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1109\/TSP.2003.815389","article-title":"Framing pyramids","volume":"51","author":"Minh N.D.","year":"2003","unstructured":"MinhN.D. and MartinV., Framing pyramids, IEEE Transactions on Signal Processing 51(9) (2003), 2329\u20132342.","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"9","key":"e_1_3_2_25_2","first-page":"1424","article-title":"Improved structures of maximally decimated directional filter banks for spatial image analysis","volume":"13","author":"Park S.I.","year":"2004","unstructured":"ParkS.I., smithM.J.T. and MersereauR.M., Improved structures of maximally decimated directional filter banks for spatial image analysis, IEEE Transactions on Signal Processing 13(9) (2004), 1424\u20131434.","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"3","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vetor networks","volume":"20","author":"Cortes C.","year":"1995","unstructured":"CortesC. and VapnikV., Support-vetor networks, Machine Learning 20(3) (1995), 273\u2013297.","journal-title":"Machine Learning"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"PlattJ. Fast training of SVMs using sequential minimal optimization Advances in Kernel Methods Support Vector Machine MIT Press Cambridge 1999. pp. 185\u2013208.","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_3_2_28_2","unstructured":"MilgramJ. CherietM. and SabourinR. One Against One\u201d or \u201cOne Against All: Which One is Better for Handwriting Recognition with SVMs? International workshop on Frontiers in handwriting Recognition Montreal Canada 2006."},{"key":"e_1_3_2_29_2","first-page":"1","article-title":"Classification using Intersection Kernel Support Vector Machines is efficient","author":"Maji S.","year":"2008","unstructured":"MajiS., BergA.C., MalikJ., Classification using Intersection Kernel Support Vector Machines is efficient, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008, pp. 1\u20138.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"issue":"4","key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s00454-004-1152-0","article-title":"Output-sensitive algorithms for computing nearest-neighbor decision boundaries","volume":"33","author":"Bremner D.","year":"2005","unstructured":"BremnerD., DemaineE., EricksonJ., et al., Output-sensitive algorithms for computing nearest-neighbor decision boundaries, Discrete and Computational Geometry 33(4) (2005), 593\u2013604.","journal-title":"Discrete and Computational Geometry"},{"key":"e_1_3_2_31_2","unstructured":"HaykinS. Neural Networks: A comprehensive foundation (2ed.). Prentice Hall 1998."},{"key":"e_1_3_2_32_2","first-page":"175","article-title":"A generalized kernel approach to dissimilarity-based classification","volume":"2","author":"Pekalska E.","year":"2001","unstructured":"PekalskaE., PaclikP. and DuinR.P.W., A generalized kernel approach to dissimilarity-based classification, Journal of Machine Learning Research 2 (2001), 175\u2013211.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.3233\/HIS-2008-5405"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-151779","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/IFS-151779","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-151779","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:12Z","timestamp":1777455552000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-151779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,31]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,8,31]]}},"alternative-id":["10.3233\/IFS-151779"],"URL":"https:\/\/doi.org\/10.3233\/ifs-151779","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,8,31]]}}}