{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:46Z","timestamp":1760242906403,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,15]],"date-time":"2016-11-15T00:00:00Z","timestamp":1479168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Project of Natural Science Foundation of Shanxi Province","award":["No. 2013011017-3"],"award-info":[{"award-number":["No. 2013011017-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the back propagation (BP) neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, although there are complex textures and illumination variations on the images of the face database named AT&amp;T, the detection accuracy rate of the proposed method can reach above 0.93. In addition, extensive simulations based on the Yale and extended Yale B datasets further verify the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/sym8110130","type":"journal-article","created":{"date-parts":[[2016,11,15]],"date-time":"2016-11-15T10:25:31Z","timestamp":1479205531000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network"],"prefix":"10.3390","volume":"8","author":[{"given":"Yajun","family":"Xu","sequence":"first","affiliation":[{"name":"College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengmei","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huifang","family":"Xu","sequence":"additional","affiliation":[{"name":"Daqin Railway Co. Ltd., Taiyuan Railway Administration, Taiyuan 030013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,15]]},"reference":[{"key":"ref_1","first-page":"579","article-title":"Facial expression recognition based on lifting wavelet and FLD","volume":"38","author":"Dong","year":"2012","journal-title":"Opt. Tech."},{"key":"ref_2","first-page":"3635","article-title":"Face recognition approach based on local medium frequency Gabor filters","volume":"34","author":"Zhou","year":"2013","journal-title":"Comput. Eng. Des."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.3788\/gzxb20134212.1448","article-title":"3D face recognition by kernel collaborative representation based on Gabor feature","volume":"42","author":"Zhan","year":"2013","journal-title":"Acta Photonica Sin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"898","DOI":"10.4028\/www.scientific.net\/AMM.733.898","article-title":"Application research on improved fusion algorithm based on BP neural network and POS","volume":"733","author":"Li","year":"2015","journal-title":"Appl. Mech. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(115)","article-title":"Artificial neural networks in hydrology. I: Preliminary concepts","volume":"5","author":"Govindaraju","year":"2000","journal-title":"J. Hydrol. Eng."},{"key":"ref_6","first-page":"4","article-title":"Applications of wavelet fuzzy neural network in approximating non-linear functions","volume":"41","author":"Shao","year":"2013","journal-title":"Comput. Dig. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.4028\/www.scientific.net\/AMR.295-297.2272","article-title":"Research on fault diagnosis for rotating machinery vibration of aero-engine based on wavelet transformation and probabilistic neural network","volume":"295\u2013297","author":"Wu","year":"2011","journal-title":"Adv. Mater. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kwolek, B. (2005, January 11\u201315). Face Detection Using Convolutional Neural Networks And Gabor Filters. Proceedings of the International Conference Artificial Neural Networks: Biological Inspirations (ICANN 2005), Warsaw, Poland.","DOI":"10.1007\/11550822_86"},{"key":"ref_9","first-page":"58","article-title":"Face detection using neural network & Gabor wavelet transform","volume":"1","author":"Kaushal","year":"2010","journal-title":"Int. J. Comput. Sci. Technol."},{"key":"ref_10","first-page":"705","article-title":"Face detection and recognition using back propagation neural network and fourier gabor filters","volume":"2","author":"Andrzej","year":"2011","journal-title":"Signal Image Process."},{"key":"ref_11","unstructured":"Yale Face Database. Available online: http:\/\/vision.ucsd.edu\/content\/yale-face-database."},{"key":"ref_12","unstructured":"Database of Faces. Available online: http:\/\/www.cl.cam.ac.uk\/research\/dtg\/attarchive\/facedatabase.html."},{"key":"ref_13","unstructured":"Extended Yale Face Database B (B+). Available online: http:\/\/vision.ucsd.edu\/content\/extended-yale-face-database-b-b."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s12938-016-0141-x","article-title":"Erratum to: Reference point detection for camera-based fingerprint image based on wavelet transformation","volume":"15","author":"Khalil","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10044-014-0388-4","article-title":"Improved nuisance attribute projection for face recognition","volume":"19","author":"Yifrach","year":"2016","journal-title":"Form. Pattern Anal. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/12.210173","article-title":"Distortion invariant object recognition in the dynamic link architecture","volume":"42","author":"Lades","year":"1993","journal-title":"IEEE Trans. Comput."},{"key":"ref_17","first-page":"187","article-title":"Analysis of house price prediction based on genetic algorithm and BP neural network","volume":"40","author":"Gao","year":"2014","journal-title":"Comput. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TPAMI.2005.92","article-title":"Acquiring Linear Subspaces for Face Recognition under Variable Lighting","volume":"27","author":"Lee","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, S. (2016). Introductory Chapter: Face Recognition-Overview, Dimensionality Reduction, and Evaluation Methods. Face Recognition-Semisupervised Classification, Subspace Projection and Evaluation Methods, InTech.","DOI":"10.5772\/63995"},{"key":"ref_20","first-page":"192","article-title":"Region based approaches and descriptors extracted from the cooccurrence matrix","volume":"3","author":"Nanni","year":"2014","journal-title":"Int. J. Latest Res. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TIP.2010.2041397","article-title":"Fusing local patterns of Gabor magnitude and phase for face recognition","volume":"19","author":"Xie","year":"2010","journal-title":"IEEE Trans. Image Process."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/8\/11\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:35:34Z","timestamp":1760211334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/8\/11\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,15]]},"references-count":21,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["sym8110130"],"URL":"https:\/\/doi.org\/10.3390\/sym8110130","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2016,11,15]]}}}