{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T01:25:18Z","timestamp":1772069118986,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,9,5]],"date-time":"2017-09-05T00:00:00Z","timestamp":1504569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The human face plays an important role in our social interaction, conveying people\u2019s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. Faces can have many variations in appearance (aging, facial expression, illumination, inaccurate alignment and pose) which continue to cause poor ability to recognize identity. The purpose of our research work is to provide an approach that contributes to resolve face identification issues with large variations of parameters such as pose, illumination, and expression. For provable outcomes, we combined two algorithms: (a) robustness local binary pattern (LBP), used for facial feature extractions; (b) k-nearest neighbor (K-NN) for image classifications. Our experiment has been conducted on the CMU PIE (Carnegie Mellon University Pose, Illumination, and Expression) face database and the LFW (Labeled Faces in the Wild) dataset. The proposed identification system shows higher performance, and also provides successful face similarity measures focus on feature extractions.<\/jats:p>","DOI":"10.3390\/jimaging3030037","type":"journal-article","created":{"date-parts":[[2017,9,5]],"date-time":"2017-09-05T11:26:19Z","timestamp":1504610779000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2723-6761","authenticated-orcid":false,"given":"Idelette","family":"Kambi Beli","sequence":"first","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, China"}]},{"given":"Chunsheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Hadid, A., and Pietikainen, M. 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Pose, Illumination and Expression invariant Face Recognition using Laplacian of Gaussian and Local Binary Pattern. Proceedings of the 5th Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, India.","DOI":"10.1109\/NUICONE.2015.7449622"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khorsheed, J.A., and Yurtkan, K. (2016, January 16\u201319). Analysis of Local Binary Patterns for Face Recognition Under Varying Facial Expressions. Proceedings of the Signal Processing and Communication Application Conference 2016, Zonguldak, Turkey.","DOI":"10.1109\/SIU.2016.7496182"},{"key":"ref_13","first-page":"46","article-title":"Face Recognition with Local Binary Patterns","volume":"5","author":"Ali","year":"2012","journal-title":"Bahria Univ. J. Inf. Commu. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ameur, B., Masmoudi, S., Derbel, A.G., and Hamida, A.B. (2016, January 21\u201323). 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