@inproceedings{
 title = {Automatic detection of facial feature points in image sequences},
 type = {inproceedings},
 year = {2011},
 keywords = {Bismuth,Cohn-Kanade database,Computational modeling,Databases,Face recognition,Feature detection,Image recognition,Normalization,Threshold,automatic facial feature point detection,binary image,eye region horizontal histograms,eyeball detection,face location,face recognition,facial image interpretation,feature extraction,image normalization,image sequence,image sequences,iris recognition,thresholding techniques},
 pages = {1-5},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6108957},
 month = {11},
 publisher = {IEEE},
 id = {3361e4ae-6765-3a19-b7e7-ec0d6b444e6f},
 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 = {6108957},
 source_type = {inproceedings},
 short_title = {Image Information Processing (ICIIP), 2011 Interna},
 private_publication = {false},
 abstract = {Detection and location of the face as well as extraction of facial features from images is an important stage for numerous facial image interpretation tasks. Detection of facial feature points, such as corners of eyes, lip corners, nostrils from the images are crucial. In this paper a method for autormatic facial feature point detection in image sequences, is introduced. The method uses image normalization, and thresholding techniques to detect 14 facial feature points. Algorithm proposed by Wolf Kienzle is used for face recognition. The detected face region is then divided into 5 relevant regions of interest, each of which is examined separately, further to detect the location of the facial feature points. In each region image is normalized with respect to brightness. Suitable threshold is set for each region, using which image is converted into binary image. Then in each region extreme ends of binary image will locate the facial feature points. In the eye region horizontal and vertical histograms are analyzed to detect eyeballs. This method when tested on Cohn-Kanade database results in recognition rate of 86%. Moreover, when tested on Informatics and Mathematical Modeling (IMM) face database which consists of tilted faces around y axis, we achieved average recognition rate of 83%.},
 bibtype = {inproceedings},
 author = {Patil, Rajesh A and Sahula, Vineet and Mandal, A. S.},
 doi = {10.1109/ICIIP.2011.6108957},
 booktitle = {2011 IEEE International Conference on Image Information Processing (ICIIP),}
}

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Detection of facial feature points, such as corners of eyes, lip corners, nostrils from the images are crucial. In this paper a method for autormatic facial feature point detection in image sequences, is introduced. The method uses image normalization, and thresholding techniques to detect 14 facial feature points. Algorithm proposed by Wolf Kienzle is used for face recognition. The detected face region is then divided into 5 relevant regions of interest, each of which is examined separately, further to detect the location of the facial feature points. In each region image is normalized with respect to brightness. Suitable threshold is set for each region, using which image is converted into binary image. Then in each region extreme ends of binary image will locate the facial feature points. In the eye region horizontal and vertical histograms are analyzed to detect eyeballs. This method when tested on Cohn-Kanade database results in recognition rate of 86%. 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S.","doi":"10.1109/ICIIP.2011.6108957","booktitle":"2011 IEEE International Conference on Image Information Processing (ICIIP),","bibtex":"@inproceedings{\n title = {Automatic detection of facial feature points in image sequences},\n type = {inproceedings},\n year = {2011},\n keywords = {Bismuth,Cohn-Kanade database,Computational modeling,Databases,Face recognition,Feature detection,Image recognition,Normalization,Threshold,automatic facial feature point detection,binary image,eye region horizontal histograms,eyeball detection,face location,face recognition,facial image interpretation,feature extraction,image normalization,image sequence,image sequences,iris recognition,thresholding techniques},\n pages = {1-5},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6108957},\n month = {11},\n publisher = {IEEE},\n id = {3361e4ae-6765-3a19-b7e7-ec0d6b444e6f},\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 = {6108957},\n source_type = {inproceedings},\n short_title = {Image Information Processing (ICIIP), 2011 Interna},\n private_publication = {false},\n abstract = {Detection and location of the face as well as extraction of facial features from images is an important stage for numerous facial image interpretation tasks. 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