@InProceedings{RameshGopaChat:2015:EySeCh,
               author = "Ramesh, Aditya and Gopalakrishnan, Anand and Chaturvedi, Ashvini",
          affiliation = "National Institute of Technology Karnataka, Surathkal, India and 
                         National Institute of Technology Karnataka, Surathkal, India and 
                         National Institute of Technology Karnataka, Surathkal, India",
                title = "Eyebrow segmentation and characterization using energy estimation 
                         and K-Means clustering",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Vieira, Thales Miranda de Almeida and Mello, Vinicius Moreira",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "eyebrow parameters, segmentation, K-means, biometric, facial 
                         expression.",
             abstract = "The eyebrow is an important feature point in a facial image. The 
                         data from a segmented eyebrow can be used as a cue for gender 
                         determination, mood analysis, facial expression recognition, 
                         non-verbal communication and biometric purposes. In this paper, we 
                         present a novel method to segment the eyebrow and characterize the 
                         state of the eyebrow based on the evaluation of a few key 
                         parameters such as thickness and archness of the eyebrow and 
                         distance of the eyebrow from the eye. Our technique involves 
                         obtaining a box containing the eye and eye brow region using 
                         Viola-Jones algorithm. We then segment out the skin region in this 
                         box by using the fact that the skin is abundant in its red 
                         component as compared to the eye and eyebrows. Further, we perform 
                         energy based thresholding to detect the darker regions in this box 
                         and then perform K-means clustering to obtain the best possible 
                         segmentation for the eyebrow.",
  conference-location = "Salvador, BA, Brazil",
      conference-year = "26-29 Aug. 2015",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3JT78H2",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JT78H2",
           targetfile = "Sibgrapi_AR_cam_ready.pdf",
        urlaccessdate = "2024, May 06"
}