{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:08:26Z","timestamp":1760609306514,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T00:00:00Z","timestamp":1596672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and\/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face\u2019s similarity\/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, while the other parts shared their parameters. A multi-batch algorithm is utilized for training. The training process takes a few hours on a single GPU. The proposed approach achieves near-human accuracy (98.9%) on the Labeled Faces in the Wild (LFW) benchmark, which is competitive with other techniques that are presented in the literature. It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). To handle the cluster quality challenge, a novel post-clustering optimization procedure is proposed. It outperforms popular clustering approaches, like K-Means and spectral by 0.098 and up to 0.344 according to F1-measure.<\/jats:p>","DOI":"10.3390\/bdcc4030019","type":"journal-article","created":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T09:41:21Z","timestamp":1596706881000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-7707","authenticated-orcid":false,"given":"Nehal K.","family":"Ahmed","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-7948","authenticated-orcid":false,"given":"Elsayed E.","family":"Hemayed","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt"},{"name":"Zewail City of Science and Technology, University of Science and Technology, Giza 12578, Egypt"}]},{"given":"Magda B.","family":"Fayek","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1145\/954339.954342","article-title":"Face recognition: A literature survey","volume":"35","author":"Zhao","year":"2003","journal-title":"ACM Comput. 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