{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:41:54Z","timestamp":1754156514748,"version":"3.41.2"},"reference-count":26,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2020,5,9]],"date-time":"2020-05-09T00:00:00Z","timestamp":1588982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2020,5,9]]},"abstract":"Purpose<\/jats:title>In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.<\/jats:p><\/jats:sec>Findings<\/jats:title>Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-02-2020-0017","type":"journal-article","created":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T09:14:30Z","timestamp":1589447670000},"page":"207-221","source":"Crossref","is-referenced-by-count":6,"title":["A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network"],"prefix":"10.1108","volume":"13","author":[{"given":"Minghua","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"10","key":"key2020070214320483900_ref001","first-page":"73","article-title":"Critical features for face recognition","volume":"182","year":"2019","journal-title":"Cognition"},{"issue":"01","key":"key2020070214320483900_ref002","first-page":"1","article-title":"PCAPooL: unsupervised feature learning for face recognition using PCA, LBP, and pyramid pooling","volume":"12","year":"2019","journal-title":"Pattern Analysis and Applications"},{"issue":"03","key":"key2020070214320483900_ref003","first-page":"1","article-title":"Analysis of color image features extraction using texture methods","volume":"17","year":"2019","journal-title":"TELKOMNIKA (Telecommunication Comput. Electron. Control)"},{"issue":"01","key":"key2020070214320483900_ref004","first-page":"170116","article-title":"A cascade face spoofing detector based on face anti-spoofing R-CNN and improved Retinex LBP","volume":"7","year":"2019","journal-title":"IEEE Access"},{"first-page":"4690","article-title":"Arcface: additive angular margin loss for deep face recognition","year":"2019","key":"key2020070214320483900_ref005"},{"issue":"1","key":"key2020070214320483900_ref006","doi-asserted-by":"crossref","first-page":"239","DOI":"10.11591\/eei.v8i1.1439","article-title":"Face recognition attendance system using Local Binary Pattern (LBP)","volume":"8","year":"2019","journal-title":"Bulletin of Electrical Engineering and Informatics"},{"first-page":"1","article-title":"A large-scale software-generated face composite sketch database","year":"2016","key":"key2020070214320483900_ref007"},{"issue":"2","key":"key2020070214320483900_ref008","first-page":"547","article-title":"Local robust sparse representation for face recognition with single sample per person","volume":"5","year":"2017","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"01","key":"key2020070214320483900_ref009","first-page":"82","article-title":"Data induced masking representation learning for face data analysis","volume":"177","year":"2019","journal-title":"Knowledge-Based Systems"},{"issue":"11","key":"key2020070214320483900_ref010","first-page":"5546","article-title":"Facial action unit detection with multilayer fused multi-task and multi-label deep learning network","volume":"13","year":"2019","journal-title":"KSII Transactions on Internet and Information Systems (TIIS)"},{"issue":"03","key":"key2020070214320483900_ref011","first-page":"1","article-title":"Deep imbalanced learning for face recognition and attribute prediction","volume":"12","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"key2020070214320483900_ref012","first-page":"7733","article-title":"Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things","volume":"22","year":"2019","journal-title":"Cluster Computing"},{"issue":"03","key":"key2020070214320483900_ref013","first-page":"453","article-title":"Dependence structure of Gabor wavelets based on copula for face recognition","volume":"137","year":"2019","journal-title":"Expert Systems with Applications"},{"issue":"08","key":"key2020070214320483900_ref014","first-page":"1","article-title":"An improved deep residual network-based semantic simultaneous localization and mapping method for monocular vision robot","volume":"12","year":"2020","journal-title":"Computational Intelligence and Neuroscience"},{"key":"key2020070214320483900_ref015","doi-asserted-by":"crossref","unstructured":"Pakhomov, D., Premachandran, V., Allan, M., Azizian, M. and Navab, N. (2019), \u201cDeep residual learning for instrument segmentation in robotic surgery\u201d, International Workshop on Machine Learning in Medical Imaging, Springer, Cham, pp. 566-573.","DOI":"10.1007\/978-3-030-32692-0_65"},{"issue":"01","key":"key2020070214320483900_ref016","first-page":"23012","article-title":"Face recognition systems under morphing attacks: a survey","volume":"7","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"key2020070214320483900_ref017","first-page":"0975","article-title":"Efficient medical image enhancement using CLAHE enhancement and wavelet fusion","volume":"167","year":"2017","journal-title":"International Journal of Computer Applications"},{"issue":"04","key":"key2020070214320483900_ref018","first-page":"127","article-title":"Collaborative representation based face classification exploiting block weighted LBP and analysis dictionary learning","volume":"88","year":"2019","journal-title":"Pattern Recognition"},{"issue":"01","key":"key2020070214320483900_ref019","first-page":"1","article-title":"Ensemble of texture and shape descriptors using support vector machine classification for face recognition","volume":"1","year":"2019","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"03","key":"key2020070214320483900_ref020","first-page":"273","article-title":"An improved combination approach based on Adaboost algorithm for wind speed time series forecasting","volume":"160","year":"2018","journal-title":"Energy Conversion and Management"},{"issue":"11","key":"key2020070214320483900_ref021","first-page":"112","article-title":"Low resolution face recognition using a two branch deep convolutional neural network architecture","volume":"139","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"11","key":"key2020070214320483900_ref022","article-title":"Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue","volume":"12","year":"2019","journal-title":"Journal of Biophotonics"},{"issue":"9","key":"key2020070214320483900_ref023","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","article-title":"Attention residual learning for skin lesion classification","volume":"38","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"first-page":"381","article-title":"Weighted multimodal biometric recognition algorithm based on histogram of contourlet oriented gradient feature description","year":"2019","key":"key2020070214320483900_ref024"},{"issue":"05","key":"key2020070214320483900_ref025","first-page":"180","article-title":"Feature agglomeration networks for single stage face detection","volume":"380","year":"2020","journal-title":"Neurocomputing"},{"first-page":"1670","article-title":"Image enhancement by deep learning network based on derived image and Retinex","year":"2019","key":"key2020070214320483900_ref026"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-02-2020-0017\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-02-2020-0017\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:06Z","timestamp":1753397646000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/13\/2\/207-221\/117825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,9]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,5,9]]}},"alternative-id":["10.1108\/IJICC-02-2020-0017"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-02-2020-0017","relation":{},"ISSN":["1756-378X"],"issn-type":[{"type":"print","value":"1756-378X"}],"subject":[],"published":{"date-parts":[[2020,5,9]]}}}