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In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved. <\/jats:p>","DOI":"10.1142\/s0219691319500322","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T13:10:10Z","timestamp":1553692210000},"page":"1950032","source":"Crossref","is-referenced-by-count":10,"title":["Gabor-oriented local order feature-based deep learning for face annotation"],"prefix":"10.1142","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8940-0016","authenticated-orcid":false,"given":"A.","family":"Kasthuri","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli 627012, India"}]},{"given":"A.","family":"Suruliandi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli 627012, India"}]},{"given":"S. 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