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In this work, starting from partitioned iterated function systems to identify the pose, different regression models are adopted to predict the angular value errors (yaw, pitch and roll axes, respectively). This method combines the fractal image compression characteristics, such as self-similar structures in order to identify similar head rotation, with regression analysis prediction. The experimental evaluation is performed on widely used benchmark datasets, i.e., Biwi and AFLW2000, and the results are compared with many existing state-of-the-art methods, demonstrating the robustness of the proposed fusion approach and excellent performance.<\/jats:p>","DOI":"10.1007\/s00138-021-01234-1","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T08:04:08Z","timestamp":1628064248000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Partitioned iterated function systems by regression models for head pose estimation"],"prefix":"10.1007","volume":"32","author":[{"given":"Andrea F.","family":"Abate","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paola","family":"Barra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5517-2198","authenticated-orcid":false,"given":"Chiara","family":"Pero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maurizio","family":"Tucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"1234_CR1","doi-asserted-by":"publisher","first-page":"64256","DOI":"10.1109\/ACCESS.2019.2917451","volume":"7","author":"AF Abate","year":"2019","unstructured":"Abate, A.F., Barra, P., Bisogni, C., Nappi, M., Ricciardi, S.: Near real-time three axis head pose estimation without training. 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