@phdthesis{
 title = {Face colour under varying illumination - analysis and applications.},
 type = {phdthesis},
 year = {2002},
 id = {7d0fb2a1-6ac6-3fc0-acb2-a93286fac560},
 created = {2019-11-19T16:28:55.017Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},
 last_modified = {2019-11-19T16:32:13.192Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:375},
 source_type = {phdthesis},
 notes = {Dissertation. Acta Univ Oul C 171, 104 p + App.},
 folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},
 private_publication = {false},
 abstract = {The colours of objects perceived by a colour camera are dependent on the illumination
conditions. For example, when the prevailing illumination condition does not correspond to
the one used in the white balancing of the camera, the object colours can change their
appearance due to the lack of colour constancy capabilities. Many methods for colour
constancy have been suggested but so far their performance has been inadequate. Faces
are common and important objects encountered in many applications. Therefore, this
thesis is dedicated to studying face colours and their robust use under real world
illumination conditions. The main thesis statement is "knowledge about an object's colour,
like skin colour changes under different illumination conditions, can be used to develop
more robust techniques against illumination changes".
Many face databases exist, and in some cases they contain colour images and even
videos. However, from the point of view of this thesis these databases have several
limitations: unavailability of spectral data related to image acquisition, undefined
illumination conditions of the acquisition, and if illumination change is present it often
means only change in illumination direction. To overcome these limitations, two
databases, a Physics-Based Face Database and a Face Video Database were created.
In addition to the images, the Physics-Based Face Database consists of spectral data
part including skin reflectances, channel responsivities of the camera and spectral power
distribution of the illumination. The images of faces are taken under four known light
sources with different white balancing illumination conditions for over 100 persons. In
addition to videos, the Face Video Database has spectral reflectances of skin for selected
persons and images taken with the same measurement arrangement as in the
Physics-Based Face Database. The images and videos are taken with several cameras. 
The databases were used to gather information about skin chromaticities and to provide
test material. The skin RGB from images were converted to different colour spaces and
the result showed that the normalized colour coordinate was among the most usable
colour spaces for skin chromaticity modelling. None of the colour spaces could eliminate
the colour shifts in chromaticity. The obtained chromaticity constraint can be
implemented as an adaptive skin colour modelling part of face tracking algorithms, like
histogram backprojection or mean shift. The performances of these adaptive algorithms
were superior compared to those using a fixed skin colour model or model adaptation
based on spatial pixel selection. Of course, there are cases when the colour cue is not
enough alone and use of other cues like motion or edge data would improve the result. It
was also demonstrated that the skin colour model can be used to segment faces and the
segmentation results depend on the background due to the method used. Also an
application for colour correction using principal component analysis and a simplified
dichromatic reflection model was shown to improve colour quality of seriously clipped
images. The results of tracking, segmentation and colour correction experiments using
the collected data validate the thesis statement.
Keywords: image colour analysis, machine vision, computer vision, skin colour, varying
lighting conditions, colour camera},
 bibtype = {phdthesis},
 author = {Martinkauppi, B}
}

{"_id":"WxecvupxyAXeWfCjj","bibbaseid":"martinkauppi-facecolourundervaryingilluminationanalysisandapplications-2002","authorIDs":[],"author_short":["Martinkauppi, B."],"bibdata":{"title":"Face colour under varying illumination - analysis and applications.","type":"phdthesis","year":"2002","id":"7d0fb2a1-6ac6-3fc0-acb2-a93286fac560","created":"2019-11-19T16:28:55.017Z","file_attached":false,"profile_id":"bddcf02d-403b-3b06-9def-6d15cc293e20","group_id":"28b2996c-b80f-3c26-be71-695caf7040ac","last_modified":"2019-11-19T16:32:13.192Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"mvg:375","source_type":"phdthesis","notes":"Dissertation. Acta Univ Oul C 171, 104 p + App.","folder_uuids":"8292f5ec-1c57-4113-a303-25778e695f8c","private_publication":false,"abstract":"The colours of objects perceived by a colour camera are dependent on the illumination\nconditions. For example, when the prevailing illumination condition does not correspond to\nthe one used in the white balancing of the camera, the object colours can change their\nappearance due to the lack of colour constancy capabilities. Many methods for colour\nconstancy have been suggested but so far their performance has been inadequate. Faces\nare common and important objects encountered in many applications. Therefore, this\nthesis is dedicated to studying face colours and their robust use under real world\nillumination conditions. The main thesis statement is \"knowledge about an object's colour,\nlike skin colour changes under different illumination conditions, can be used to develop\nmore robust techniques against illumination changes\".\nMany face databases exist, and in some cases they contain colour images and even\nvideos. However, from the point of view of this thesis these databases have several\nlimitations: unavailability of spectral data related to image acquisition, undefined\nillumination conditions of the acquisition, and if illumination change is present it often\nmeans only change in illumination direction. To overcome these limitations, two\ndatabases, a Physics-Based Face Database and a Face Video Database were created.\nIn addition to the images, the Physics-Based Face Database consists of spectral data\npart including skin reflectances, channel responsivities of the camera and spectral power\ndistribution of the illumination. The images of faces are taken under four known light\nsources with different white balancing illumination conditions for over 100 persons. In\naddition to videos, the Face Video Database has spectral reflectances of skin for selected\npersons and images taken with the same measurement arrangement as in the\nPhysics-Based Face Database. The images and videos are taken with several cameras. \nThe databases were used to gather information about skin chromaticities and to provide\ntest material. The skin RGB from images were converted to different colour spaces and\nthe result showed that the normalized colour coordinate was among the most usable\ncolour spaces for skin chromaticity modelling. None of the colour spaces could eliminate\nthe colour shifts in chromaticity. The obtained chromaticity constraint can be\nimplemented as an adaptive skin colour modelling part of face tracking algorithms, like\nhistogram backprojection or mean shift. The performances of these adaptive algorithms\nwere superior compared to those using a fixed skin colour model or model adaptation\nbased on spatial pixel selection. Of course, there are cases when the colour cue is not\nenough alone and use of other cues like motion or edge data would improve the result. It\nwas also demonstrated that the skin colour model can be used to segment faces and the\nsegmentation results depend on the background due to the method used. Also an\napplication for colour correction using principal component analysis and a simplified\ndichromatic reflection model was shown to improve colour quality of seriously clipped\nimages. The results of tracking, segmentation and colour correction experiments using\nthe collected data validate the thesis statement.\nKeywords: image colour analysis, machine vision, computer vision, skin colour, varying\nlighting conditions, colour camera","bibtype":"phdthesis","author":"Martinkauppi, B","bibtex":"@phdthesis{\n title = {Face colour under varying illumination - analysis and applications.},\n type = {phdthesis},\n year = {2002},\n id = {7d0fb2a1-6ac6-3fc0-acb2-a93286fac560},\n created = {2019-11-19T16:28:55.017Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},\n last_modified = {2019-11-19T16:32:13.192Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {mvg:375},\n source_type = {phdthesis},\n notes = {Dissertation. Acta Univ Oul C 171, 104 p + App.},\n folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},\n private_publication = {false},\n abstract = {The colours of objects perceived by a colour camera are dependent on the illumination\nconditions. For example, when the prevailing illumination condition does not correspond to\nthe one used in the white balancing of the camera, the object colours can change their\nappearance due to the lack of colour constancy capabilities. Many methods for colour\nconstancy have been suggested but so far their performance has been inadequate. Faces\nare common and important objects encountered in many applications. Therefore, this\nthesis is dedicated to studying face colours and their robust use under real world\nillumination conditions. The main thesis statement is \"knowledge about an object's colour,\nlike skin colour changes under different illumination conditions, can be used to develop\nmore robust techniques against illumination changes\".\nMany face databases exist, and in some cases they contain colour images and even\nvideos. However, from the point of view of this thesis these databases have several\nlimitations: unavailability of spectral data related to image acquisition, undefined\nillumination conditions of the acquisition, and if illumination change is present it often\nmeans only change in illumination direction. To overcome these limitations, two\ndatabases, a Physics-Based Face Database and a Face Video Database were created.\nIn addition to the images, the Physics-Based Face Database consists of spectral data\npart including skin reflectances, channel responsivities of the camera and spectral power\ndistribution of the illumination. The images of faces are taken under four known light\nsources with different white balancing illumination conditions for over 100 persons. In\naddition to videos, the Face Video Database has spectral reflectances of skin for selected\npersons and images taken with the same measurement arrangement as in the\nPhysics-Based Face Database. The images and videos are taken with several cameras. \nThe databases were used to gather information about skin chromaticities and to provide\ntest material. The skin RGB from images were converted to different colour spaces and\nthe result showed that the normalized colour coordinate was among the most usable\ncolour spaces for skin chromaticity modelling. None of the colour spaces could eliminate\nthe colour shifts in chromaticity. The obtained chromaticity constraint can be\nimplemented as an adaptive skin colour modelling part of face tracking algorithms, like\nhistogram backprojection or mean shift. The performances of these adaptive algorithms\nwere superior compared to those using a fixed skin colour model or model adaptation\nbased on spatial pixel selection. Of course, there are cases when the colour cue is not\nenough alone and use of other cues like motion or edge data would improve the result. It\nwas also demonstrated that the skin colour model can be used to segment faces and the\nsegmentation results depend on the background due to the method used. Also an\napplication for colour correction using principal component analysis and a simplified\ndichromatic reflection model was shown to improve colour quality of seriously clipped\nimages. The results of tracking, segmentation and colour correction experiments using\nthe collected data validate the thesis statement.\nKeywords: image colour analysis, machine vision, computer vision, skin colour, varying\nlighting conditions, colour camera},\n bibtype = {phdthesis},\n author = {Martinkauppi, B}\n}","author_short":["Martinkauppi, B."],"bibbaseid":"martinkauppi-facecolourundervaryingilluminationanalysisandapplications-2002","role":"author","urls":{},"downloads":0},"bibtype":"phdthesis","creationDate":"2019-11-19T16:11:29.090Z","downloads":0,"keywords":[],"search_terms":["face","colour","under","varying","illumination","analysis","applications","martinkauppi"],"title":"Face colour under varying illumination - analysis and applications.","year":2002}