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Video database for face feature recognition
Stříteský, Jan ; Říha, Kamil (referee) ; Vlach, Jan (advisor)
This work compares face databases freely accessible on the Internet which are suitable for the testing of developed algorithms for facial features recognition in a picture. In the course of the work’s project a new face video database was created, encompassing a total of 51 samples. The video database includes a simple application suitable for searching through its contents. In order to limit the size of the video database, compression of individual picture samples was conducted. The created video database can be implemented for testing algorithms for facial features recognition (e.g. face detection in a video, identification of a person, detection of eye blinking, speech recognition).
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3D Face Reconstruction Based on 2D Image
Karhánek, Martin ; Láník, Aleš (referee) ; Částek, Petr (advisor)
This work deals with procedures that enables you reconstruct 3D face from a 2D picture. It describes ways to analyze an input picture, such as a face and facial features localization, on which this reconstruction builds. A reader is getting into a morphable model, its creation from static database data and and its usage on a reconstruction, more detailed later on, this morphable model is a building block of mentioned methods. This work contains also an algorithm implementation based on this model.
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Facial Features Detection and Grouping
Del Favero, Robert ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This bachelor's thesis deals with facial features detection in images. Especially, a skin color detection algorithm is addressed. This method find all pixels corresponding to the skin color and clusters them into candidate face regions by the help of morfological operations. Afterwards, using the Sobel operator, all facial features are located accurately. Implementation of the proposed facial feature detector based on the well known OpenCV library is presented too.
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Genetic factors influencing the characteristic facial features of people with psychical disorders
Frajbišová, Aneta ; Šolc, Roman (advisor) ; Kočandrlová, Karolina (referee)
The facial part of the head of the embryo is formed alongside with the brain from the same precursors. The face is formed from the neural crest cells, which arise from the neuroepithelium. This means that if there is some kind of disruption in the early development of the brain, it will be shown in the face. The neuroepithelium induces the expression of many important genetic factors for the formation of the face. For example PAX3, Dsl-1, HMGN1. However, environmental factors also have an impact on the final look of the face. The environmental factors are for example diet or the way of breathing. Persons with the syndromatic psychic disorders have well known and researched facial morphology compared to persons with asyndromatic psychic disorders such as schizophrenia, ASD, OCD and bipolar disorder, which are still the object of many studies. Genetic factors that have an impact on facial dysmorphology, are usually genes that have their main role in the central nervous system or they indirectly impact through signalling pathways on other genes, which are known to have an impact on the face such as Fgf genes. The goal of this thesis is to determine these genetic factors.
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3D Face Reconstruction Based on 2D Image
Karhánek, Martin ; Láník, Aleš (referee) ; Částek, Petr (advisor)
This work deals with procedures that enables you reconstruct 3D face from a 2D picture. It describes ways to analyze an input picture, such as a face and facial features localization, on which this reconstruction builds. A reader is getting into a morphable model, its creation from static database data and and its usage on a reconstruction, more detailed later on, this morphable model is a building block of mentioned methods. This work contains also an algorithm implementation based on this model.
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Facial Features Detection and Grouping
Del Favero, Robert ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This bachelor's thesis deals with facial features detection in images. Especially, a skin color detection algorithm is addressed. This method find all pixels corresponding to the skin color and clusters them into candidate face regions by the help of morfological operations. Afterwards, using the Sobel operator, all facial features are located accurately. Implementation of the proposed facial feature detector based on the well known OpenCV library is presented too.
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Facial Expression Recognition
Král, Jiří ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
Many views to facial expression recognition exist. This work presents one of approaches. Existing methods of human face representation by model are discussed. The AAM method, where final appearance model is created from model of shape and model of texture is proposed. Model of shape and model of texture is created by statistic analysis. Using this representation, an effective method is achieved that is complexity of information for searched face in static image. Choice and combination of suitable features for classification of facial expression is principle for facial expression recognition based on AAM. Two approaches of facial expression classification are compared. Classification based on LDA and classification based on SVM. These methods with necessary face localization using AdaBoost form an automated face recognizer in image.
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Video database for face feature recognition
Stříteský, Jan ; Říha, Kamil (referee) ; Vlach, Jan (advisor)
This work compares face databases freely accessible on the Internet which are suitable for the testing of developed algorithms for facial features recognition in a picture. In the course of the work’s project a new face video database was created, encompassing a total of 51 samples. The video database includes a simple application suitable for searching through its contents. In order to limit the size of the video database, compression of individual picture samples was conducted. The created video database can be implemented for testing algorithms for facial features recognition (e.g. face detection in a video, identification of a person, detection of eye blinking, speech recognition).
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