{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T09:55:51Z","timestamp":1648720551258},"reference-count":44,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:p> There remain outstanding challenges for improving accuracy of multi-feature information for head-pose and gaze estimation. The proposed framework employs discriminative analysis for head-pose and gaze estimation using kernel discriminative multiple canonical correlation analysis (K-DMCCA). The feature extraction component of the framework includes spatial indexing, statistical and geometrical elements. Head-pose and gaze estimation is constructed by feature aggregation and transforming features into a higher dimensional space using K-DMCCA for accurate estimation. The two main contributions are: Enhancing fusion performance through the use of kernel-based DMCCA, and by introducing an improved iris region descriptor based on quadtree. The overall approach is also inclusive of statistical and geometrical indexing that are calibration free (does not require any subsequent adjustment). We validate the robustness of the proposed framework across a wide variety of datasets, which consist of different modalities (RGB and Depth), constraints (wide range of head-poses, not only frontal), quality (accurately labelled for validation), occlusion (due to glasses, hair bang, facial hair) and illumination. Our method achieved an accurate head-pose and gaze estimation of 4.8<jats:sup>\u2218<\/jats:sup> using Cave, 4.6<jats:sup>\u2218<\/jats:sup> using MPII, 5.1<jats:sup>\u2218<\/jats:sup> using ACS, 5.9<jats:sup>\u2218<\/jats:sup> using EYEDIAP, 4.3<jats:sup>\u2218<\/jats:sup> using OSLO and 4.6<jats:sup>\u2218<\/jats:sup> using UULM datasets. <\/jats:p>","DOI":"10.1142\/s1793351x20500014","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T09:00:30Z","timestamp":1591693230000},"page":"107-135","source":"Crossref","is-referenced-by-count":0,"title":["Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion"],"prefix":"10.1142","volume":"14","author":[{"given":"Salah","family":"Rabba","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Kyan","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Gao","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azhar","family":"Quddus","sequence":"additional","affiliation":[{"name":"Alcohol Countermeasure Systems, ACS Corporation, 60 International Blvd, Etobicoke, Ontario, M9W 6J2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Shahidi","family":"Zandi","sequence":"additional","affiliation":[{"name":"Alcohol Countermeasure Systems, ACS Corporation, 60 International Blvd, Etobicoke, Ontario, M9W 6J2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Guan","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"S1793351X20500014BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.30"},{"key":"S1793351X20500014BIB002","author":"Zhang X.","year":"2017","journal-title":"IEEE Computer Vis. 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