@article{
 title = {Supervised learning approach to remote heart rate estimation from facial videos},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 id = {c31a355d-8bf2-3c29-809f-3b1a14ca8d2b},
 created = {2018-02-19T20:27:15.964Z},
 file_attached = {true},
 profile_id = {f18f97b7-60c6-33d4-b944-c08e6c28fa12},
 group_id = {2d721da3-627a-3ca3-862a-cd2d130d920f},
 last_modified = {2018-07-10T07:07:13.410Z},
 read = {true},
 starred = {true},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Osman2015},
 folder_uuids = {4a8662e1-5b1b-46fc-b429-46baa4e1d907,81855f6c-11eb-4375-a02e-a3cd04030ca0},
 private_publication = {false},
 abstract = {A supervised machine learning approach to remote video-based heart rate (HR) estimation is proposed. We demonstrate the possibility of training a discriminative statistical model to estimate the Blood Volume Pulse signal (BVP) from the human face using ambient light and any offthe- shelf webcam. The proposed algorithm is 120 times faster than state of the art approach and returns a confidence metric to evaluate the HR estimates plausibility. The algorithm was evaluated against the state-of-the-art on 120 minutes of face videos, the largest video-based heart rate evaluation to date. The evaluation results showed a 53% decrease in the Root Mean Squared Error (RMSE) compared to state-of-the-art.},
 bibtype = {article},
 author = {Osman, Ahmed and Turcot, Jay and Kaliouby, Rana El},
 journal = {2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015}
}

{"_id":"7wof2kjL3NjZgeqeM","bibbaseid":"osman-turcot-kaliouby-supervisedlearningapproachtoremoteheartrateestimationfromfacialvideos-2015","downloads":0,"creationDate":"2018-10-05T13:09:54.315Z","title":"Supervised learning approach to remote heart rate estimation from facial videos","author_short":["Osman, A.","Turcot, J.","Kaliouby, R., E."],"year":2015,"bibtype":"article","biburl":null,"bibdata":{"title":"Supervised learning approach to remote heart rate estimation from facial videos","type":"article","year":"2015","identifiers":"[object Object]","id":"c31a355d-8bf2-3c29-809f-3b1a14ca8d2b","created":"2018-02-19T20:27:15.964Z","file_attached":"true","profile_id":"f18f97b7-60c6-33d4-b944-c08e6c28fa12","group_id":"2d721da3-627a-3ca3-862a-cd2d130d920f","last_modified":"2018-07-10T07:07:13.410Z","read":"true","starred":"true","authored":false,"confirmed":"true","hidden":false,"citation_key":"Osman2015","folder_uuids":"4a8662e1-5b1b-46fc-b429-46baa4e1d907,81855f6c-11eb-4375-a02e-a3cd04030ca0","private_publication":false,"abstract":"A supervised machine learning approach to remote video-based heart rate (HR) estimation is proposed. We demonstrate the possibility of training a discriminative statistical model to estimate the Blood Volume Pulse signal (BVP) from the human face using ambient light and any offthe- shelf webcam. The proposed algorithm is 120 times faster than state of the art approach and returns a confidence metric to evaluate the HR estimates plausibility. The algorithm was evaluated against the state-of-the-art on 120 minutes of face videos, the largest video-based heart rate evaluation to date. The evaluation results showed a 53% decrease in the Root Mean Squared Error (RMSE) compared to state-of-the-art.","bibtype":"article","author":"Osman, Ahmed and Turcot, Jay and Kaliouby, Rana El","journal":"2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015","bibtex":"@article{\n title = {Supervised learning approach to remote heart rate estimation from facial videos},\n type = {article},\n year = {2015},\n identifiers = {[object Object]},\n id = {c31a355d-8bf2-3c29-809f-3b1a14ca8d2b},\n created = {2018-02-19T20:27:15.964Z},\n file_attached = {true},\n profile_id = {f18f97b7-60c6-33d4-b944-c08e6c28fa12},\n group_id = {2d721da3-627a-3ca3-862a-cd2d130d920f},\n last_modified = {2018-07-10T07:07:13.410Z},\n read = {true},\n starred = {true},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Osman2015},\n folder_uuids = {4a8662e1-5b1b-46fc-b429-46baa4e1d907,81855f6c-11eb-4375-a02e-a3cd04030ca0},\n private_publication = {false},\n abstract = {A supervised machine learning approach to remote video-based heart rate (HR) estimation is proposed. We demonstrate the possibility of training a discriminative statistical model to estimate the Blood Volume Pulse signal (BVP) from the human face using ambient light and any offthe- shelf webcam. The proposed algorithm is 120 times faster than state of the art approach and returns a confidence metric to evaluate the HR estimates plausibility. The algorithm was evaluated against the state-of-the-art on 120 minutes of face videos, the largest video-based heart rate evaluation to date. The evaluation results showed a 53% decrease in the Root Mean Squared Error (RMSE) compared to state-of-the-art.},\n bibtype = {article},\n author = {Osman, Ahmed and Turcot, Jay and Kaliouby, Rana El},\n journal = {2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015}\n}","author_short":["Osman, A.","Turcot, J.","Kaliouby, R., E."],"urls":{"Paper":"https://bibbase.org/service/mendeley/b14f907e-b397-3956-8196-166e91ca3313/file/25844d83-8e98-75b9-5192-5cb387d32f3a/2015-Supervised_learning_approach_to_remote_heart_rate_estimation_from_facial_videos.pdf.pdf"},"bibbaseid":"osman-turcot-kaliouby-supervisedlearningapproachtoremoteheartrateestimationfromfacialvideos-2015","role":"author","downloads":0},"search_terms":["supervised","learning","approach","remote","heart","rate","estimation","facial","videos","osman","turcot","kaliouby"],"keywords":[],"authorIDs":[]}