{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:51:48Z","timestamp":1760241108494,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T00:00:00Z","timestamp":1574899200000},"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>A fundamental problem in computer vision is face detection. In this paper, an experimentally derived ensemble made by a set of six face detectors is presented that maximizes the number of true positives while simultaneously reducing the number of false positives produced by the ensemble. False positives are removed using different filtering steps based primarily on the characteristics of the depth map related to the subwindows of the whole image that contain candidate faces. A new filtering approach based on processing the image with different wavelets is also proposed here. The experimental results show that the applied filtering steps used in our best ensemble reduce the number of false positives without decreasing the detection rate. This finding is validated on a combined dataset composed of four others for a total of 549 images, including 614 upright frontal faces acquired in unconstrained environments. The dataset provides both 2D and depth data. For further validation, the proposed ensemble is tested on the well-known BioID benchmark dataset, where it obtains a 100% detection rate with an acceptable number of false positives.<\/jats:p>","DOI":"10.3390\/s19235242","type":"journal-article","created":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T10:54:10Z","timestamp":1574938450000},"page":"5242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Face Detection Ensemble with Methods Using Depth Information to Filter False Positives"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3502-7209","authenticated-orcid":false,"given":"Loris","family":"Nanni","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35131 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7664-6930","authenticated-orcid":false,"given":"Sheryl","family":"Brahnam","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Cybersecurity, Missouri State University, 901 S. National Street, Springfield, MO 65804, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0290-7354","authenticated-orcid":false,"given":"Alessandra","family":"Lumini","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica\u2014Scienza e Ingegneria, Universit\u00e0 di Bologna, Via Sacchi 3, 47521 Cesena, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/TPAMI.2008.52","article-title":"A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions","volume":"31","author":"Zeng","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/TPAMI.2017.2778152","article-title":"Face Alignment in Full Pose Range: A 3D Total Solution","volume":"41","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. 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