{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:03:11Z","timestamp":1776884591480,"version":"3.51.2"},"reference-count":63,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T00:00:00Z","timestamp":1612051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, we are witnessing the wide diffusion of active depth sensors. However, the generalization capabilities and performance of the deep face recognition approaches that are based on depth data are hindered by the different sensor technologies and the currently available depth-based datasets, which are limited in size and acquired through the same device. In this paper, we present an analysis on the use of depth maps, as obtained by active depth sensors and deep neural architectures for the face recognition task. We compare different depth data representations (depth and normal images, voxels, point clouds), deep models (two-dimensional and three-dimensional Convolutional Neural Networks, PointNet-based networks), and pre-processing and normalization techniques in order to determine the configuration that maximizes the recognition accuracy and is capable of generalizing better on unseen data and novel acquisition settings. Extensive intra- and cross-dataset experiments, which were performed on four public databases, suggest that representations and methods that are based on normal images and point clouds perform and generalize better than other 2D and 3D alternatives. Moreover, we propose a novel challenging dataset, namely MultiSFace, in order to specifically analyze the influence of the depth map quality and the acquisition distance on the face recognition accuracy.<\/jats:p>","DOI":"10.3390\/s21030944","type":"journal-article","created":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T21:31:56Z","timestamp":1612128716000},"page":"944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Systematic Comparison of Depth Map Representations for Face Recognition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9821-2014","authenticated-orcid":false,"given":"Stefano","family":"Pini","sequence":"first","affiliation":[{"name":"DIEF\u2014Dipartimento di Ingegneria Enzo Ferrari, Universit\u00e0 Degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2441-7524","authenticated-orcid":false,"given":"Guido","family":"Borghi","sequence":"additional","affiliation":[{"name":"DISI\u2014Dipartimento di Informatica-Scienza e Ingegneria, Universit\u00e0 di Bologna, 47521 Cesena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1046-6870","authenticated-orcid":false,"given":"Roberto","family":"Vezzani","sequence":"additional","affiliation":[{"name":"DIEF\u2014Dipartimento di Ingegneria Enzo Ferrari, Universit\u00e0 Degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy"},{"name":"AIRI\u2014Artificial Intelligence Research and Innovation Center, Universit\u00e0 Degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6329-6756","authenticated-orcid":false,"given":"Davide","family":"Maltoni","sequence":"additional","affiliation":[{"name":"DISI\u2014Dipartimento di Informatica-Scienza e Ingegneria, Universit\u00e0 di Bologna, 47521 Cesena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2239-283X","authenticated-orcid":false,"given":"Rita","family":"Cucchiara","sequence":"additional","affiliation":[{"name":"DIEF\u2014Dipartimento di Ingegneria Enzo Ferrari, Universit\u00e0 Degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy"},{"name":"AIRI\u2014Artificial Intelligence Research and Innovation Center, Universit\u00e0 Degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Niannan, X., and Zafeiriou, S. 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