{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:57:45Z","timestamp":1767085065846,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1900087"],"award-info":[{"award-number":["1900087"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.<\/jats:p>","DOI":"10.3390\/computers10090113","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T10:31:34Z","timestamp":1631269894000},"page":"113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms"],"prefix":"10.3390","volume":"10","author":[{"given":"James","family":"Coe","sequence":"first","affiliation":[{"name":"Department of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2427-9152","authenticated-orcid":false,"given":"Mustafa","family":"Atay","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130751","DOI":"10.1109\/ACCESS.2020.3006051","article-title":"Investigating Bias in Facial Analysis Systems: A Systematic Review","volume":"8","author":"Khalil","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1109\/TIFS.2012.2214212","article-title":"Face Recognition Performance: Role of Demographic Information","volume":"7","author":"Klare","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hupont, I., and Fernandez, C. (2019, January 14\u201318). DemogPairs: Quantifying the Impact of Demographic Imbalance in Deep Face Recognition. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756625"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alhindi, T.J., Kalra, S., Ng, K.H., Afrin, A., and Tizhoosh, H.R. (2018, January 8\u201313). Comparing LBP, HOG and Deep Features for Classification of Histopathology Images. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489329"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Loo, E.K., Lim, T.S., Ong, L.Y., and Lim, C.H. (2018, January 9\u201310). The Influence of Ethnicity in Facial Gender Estimation. 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Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC), International Association for Automation and Robotics in Construction (IAARC), Berlin, Germany.","DOI":"10.22260\/ISARC2018\/0094"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/9\/113\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:23Z","timestamp":1760166023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/9\/113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":29,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["computers10090113"],"URL":"https:\/\/doi.org\/10.3390\/computers10090113","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2021,9,10]]}}}