{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:21:46Z","timestamp":1762953706719,"version":"build-2065373602"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:p>Person re-identification is one of the indispensable elements for visual surveillance. It assigns consistent labeling for the same person within the field of view of the same camera or even across multiple cameras. While handcrafted feature extraction is certainly one way of approaching this problem, in many cases, these features are becoming more and more complex. Besides, training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. This paper explores the following three main strategies for solving the person re-identification problem: (i) using handcrafted features, (ii) using transfer learning based on a pre-trained deep CNN (trained for object categorization) and (iii) training a deep CNN from scratch. Our experiments consistently demonstrated that: (1) The handcrafted features may still have favorable characteristics and benefits especially in cases where the learning database is not sufficient to train a deep network. (2) A fully trained Siamese CNN outperforms handcrafted approaches and the combination of pre-trained CNN with different re-identification processes. (3) Moreover, our experiments demonstrated that pre-trained features and handcrafted features perform equally well. These experiments have also revealed the most discriminative parts in the human body.<\/jats:p>","DOI":"10.1142\/s0218001420550095","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T06:37:40Z","timestamp":1558075060000},"page":"2055009","source":"Crossref","is-referenced-by-count":11,"title":["Learned versus Handcrafted Features for Person Re-identification"],"prefix":"10.1142","volume":"34","author":[{"given":"C.","family":"Chahla","sequence":"first","affiliation":[{"name":"University of Technology of Troyes, 12 Rue Marie Curie, CS 42060, 10004 Troyes CEDEX, France"},{"name":"Lebanese University, P.\u00a0O.\u00a0Box 6573\/14 Badaro, Museum, Beirut, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H.","family":"Snoussi","sequence":"additional","affiliation":[{"name":"University of Technology of Troyes, 12 Rue Marie Curie, CS 42060, 10004 Troyes CEDEX, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Abdallah","sequence":"additional","affiliation":[{"name":"University of Technology of Troyes, 12 Rue Marie Curie, CS 42060, 10004 Troyes CEDEX, France"},{"name":"Lebanese University, P.\u00a0O.\u00a0Box 6573\/14 Badaro, Museum, Beirut, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Dornaika","sequence":"additional","affiliation":[{"name":"University of the Basque Country UPV\/EHU, San Sebastian, Basque Autonomous Community, Spain"},{"name":"IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro #3, 6 Solairua, 48013 Bilbao, Bizkaia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,8,9]]},"reference":[{"key":"S0218001420550095BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001493000339"},{"key":"S0218001420550095BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/SENSET.2017.8125063"},{"key":"S0218001420550095BIB003","doi-asserted-by":"publisher","DOI":"10.1049\/ic.2016.0089"},{"key":"S0218001420550095BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.11.004"},{"key":"S0218001420550095BIB005","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273523"},{"key":"S0218001420550095BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"S0218001420550095BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.01.035"},{"key":"S0218001420550095BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"S0218001420550095BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2437384"},{"key":"S0218001420550095BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459197"},{"key":"S0218001420550095BIB011","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"S0218001420550095BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.242"},{"key":"S0218001420550095BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"S0218001420550095BIB014","first-page":"1097","volume-title":"Proc. 25th Int. 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