@inproceedings{
 title = {Facial Expression Recognition in Image Sequences Using Active Shape Model and SVM},
 type = {inproceedings},
 year = {2011},
 keywords = {Adaptation models,Candide wire frame model,Computational modeling,Face,Image sequences,SVM,Support vector machines,Vectors,Wires,active appearance algorithm,active appearance model,active shape model,candide wire frame model,computational geometry,face recognition,facial expression intensity,facial expression recognition,geometrical displacement,image sequences,support vector machine,support vector machines,svm,video frames,video signal processing},
 pages = {168-173},
 volume = {0},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6131234},
 month = {11},
 publisher = {IEEE},
 id = {6715abe8-8efe-3580-9ecf-6716d2c92be3},
 created = {2016-04-21T16:39:32.000Z},
 accessed = {2015-12-15},
 file_attached = {true},
 profile_id = {03d2ca17-6bde-3cfe-95de-fcbe4f21507b},
 last_modified = {2017-03-14T01:22:09.162Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Patil2011b},
 short_title = {Computer Modeling and Simulation (EMS), 2011 Fifth},
 private_publication = {false},
 abstract = {This paper introduces a method for automatic facial expression recognition in image sequences, which make use of Candide wire frame model and active appearance algorithm for tracking, and support vector machine for classification. Candide wire frame model is adapted properly on the first frame of face image sequence. Facial features in subsequent frames of image sequence are tracked using active appearance algorithm. The algorithm adapts Candide wire frame model to the face in each of the frames and tracks the grid in consecutive video frames over time. Last frame of image sequence corresponds to greatest facial expression intensity. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the support vector machine, which classifies facial expression into one of the class such as happy, surprise, sad, anger, disgust and fear.},
 bibtype = {inproceedings},
 author = {Patil, Rajesh A. and Sahula, Vineet and Mandal, A.S. S},
 doi = {10.1109/EMS.2011.25},
 booktitle = {2011 UKSim 5th European Symposium on Computer Modeling and Simulation}
}

{"_id":"p9ZRpNSQCsPA7JnCc","bibbaseid":"patil-sahula-mandal-facialexpressionrecognitioninimagesequencesusingactiveshapemodelandsvm-2011","author_short":["Patil, R., A.","Sahula, V.","Mandal, A., S."],"bibdata":{"title":"Facial Expression Recognition in Image Sequences Using Active Shape Model and SVM","type":"inproceedings","year":"2011","keywords":"Adaptation models,Candide wire frame model,Computational modeling,Face,Image sequences,SVM,Support vector machines,Vectors,Wires,active appearance algorithm,active appearance model,active shape model,candide wire frame model,computational geometry,face recognition,facial expression intensity,facial expression recognition,geometrical displacement,image sequences,support vector machine,support vector machines,svm,video frames,video signal processing","pages":"168-173","volume":"0","websites":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6131234","month":"11","publisher":"IEEE","id":"6715abe8-8efe-3580-9ecf-6716d2c92be3","created":"2016-04-21T16:39:32.000Z","accessed":"2015-12-15","file_attached":"true","profile_id":"03d2ca17-6bde-3cfe-95de-fcbe4f21507b","last_modified":"2017-03-14T01:22:09.162Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Patil2011b","short_title":"Computer Modeling and Simulation (EMS), 2011 Fifth","private_publication":false,"abstract":"This paper introduces a method for automatic facial expression recognition in image sequences, which make use of Candide wire frame model and active appearance algorithm for tracking, and support vector machine for classification. Candide wire frame model is adapted properly on the first frame of face image sequence. Facial features in subsequent frames of image sequence are tracked using active appearance algorithm. The algorithm adapts Candide wire frame model to the face in each of the frames and tracks the grid in consecutive video frames over time. Last frame of image sequence corresponds to greatest facial expression intensity. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the support vector machine, which classifies facial expression into one of the class such as happy, surprise, sad, anger, disgust and fear.","bibtype":"inproceedings","author":"Patil, Rajesh A. and Sahula, Vineet and Mandal, A.S. S","doi":"10.1109/EMS.2011.25","booktitle":"2011 UKSim 5th European Symposium on Computer Modeling and Simulation","bibtex":"@inproceedings{\n title = {Facial Expression Recognition in Image Sequences Using Active Shape Model and SVM},\n type = {inproceedings},\n year = {2011},\n keywords = {Adaptation models,Candide wire frame model,Computational modeling,Face,Image sequences,SVM,Support vector machines,Vectors,Wires,active appearance algorithm,active appearance model,active shape model,candide wire frame model,computational geometry,face recognition,facial expression intensity,facial expression recognition,geometrical displacement,image sequences,support vector machine,support vector machines,svm,video frames,video signal processing},\n pages = {168-173},\n volume = {0},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6131234},\n month = {11},\n publisher = {IEEE},\n id = {6715abe8-8efe-3580-9ecf-6716d2c92be3},\n created = {2016-04-21T16:39:32.000Z},\n accessed = {2015-12-15},\n file_attached = {true},\n profile_id = {03d2ca17-6bde-3cfe-95de-fcbe4f21507b},\n last_modified = {2017-03-14T01:22:09.162Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Patil2011b},\n short_title = {Computer Modeling and Simulation (EMS), 2011 Fifth},\n private_publication = {false},\n abstract = {This paper introduces a method for automatic facial expression recognition in image sequences, which make use of Candide wire frame model and active appearance algorithm for tracking, and support vector machine for classification. Candide wire frame model is adapted properly on the first frame of face image sequence. Facial features in subsequent frames of image sequence are tracked using active appearance algorithm. The algorithm adapts Candide wire frame model to the face in each of the frames and tracks the grid in consecutive video frames over time. Last frame of image sequence corresponds to greatest facial expression intensity. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the support vector machine, which classifies facial expression into one of the class such as happy, surprise, sad, anger, disgust and fear.},\n bibtype = {inproceedings},\n author = {Patil, Rajesh A. and Sahula, Vineet and Mandal, A.S. S},\n doi = {10.1109/EMS.2011.25},\n booktitle = {2011 UKSim 5th European Symposium on Computer Modeling and Simulation}\n}","author_short":["Patil, R., A.","Sahula, V.","Mandal, A., S."],"urls":{"Paper":"https://bibbase.org/service/mendeley/03d2ca17-6bde-3cfe-95de-fcbe4f21507b/file/0c7834fd-797c-8455-665a-ca36b1994043/2011-Facial_Expression_Recognition_in_Image_Sequences_Using_Active_Shape_Model_and_SVM.pdf.pdf","Website":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6131234"},"biburl":"https://bibbase.org/service/mendeley/03d2ca17-6bde-3cfe-95de-fcbe4f21507b","bibbaseid":"patil-sahula-mandal-facialexpressionrecognitioninimagesequencesusingactiveshapemodelandsvm-2011","role":"author","keyword":["Adaptation models","Candide wire frame model","Computational modeling","Face","Image sequences","SVM","Support vector machines","Vectors","Wires","active appearance algorithm","active appearance model","active shape model","candide wire frame model","computational geometry","face recognition","facial expression intensity","facial expression recognition","geometrical displacement","image sequences","support vector machine","support vector machines","svm","video frames","video signal processing"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/03d2ca17-6bde-3cfe-95de-fcbe4f21507b","dataSources":["foRZzjCMmTPmTF8eH","ya2CyA73rpZseyrZ8"],"keywords":["adaptation models","candide wire frame model","computational modeling","face","image sequences","svm","support vector machines","vectors","wires","active appearance algorithm","active appearance model","active shape model","candide wire frame model","computational geometry","face recognition","facial expression intensity","facial expression recognition","geometrical displacement","image sequences","support vector machine","support vector machines","svm","video frames","video signal processing"],"search_terms":["facial","expression","recognition","image","sequences","using","active","shape","model","svm","patil","sahula","mandal"],"title":"Facial Expression Recognition in Image Sequences Using Active Shape Model and SVM","year":2011}