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Face Recognition
Duban, Michal ; Beran, Vítězslav (referee) ; Smrž, Pavel (advisor)
This Bachelor's thesis aimed to develop an application, executable by the robot PR2, capable of automatical recognizing and memorizing human faces which are displayed successively on video recorded by cameras placed on the robot.
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Algorithm for Head Comparison in Non-Standard Views
Wysoglad, Jaromír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
The goal of this work is to create an algorithm for human head comparison. The algorithm is able to compare heads in a lot of different positions, but the heads, that are being compared, must be in the same position. At first the algorithm uses some freely available detectors for detecting heads and head parts. Then a histogram of oriented gradients is computed for each part of each head and by comparing them the algorithm finds out the dissimilarity of the heads. From the testing set of 30 pictures the algorithm is able to successfully detect heads on 26 pictures. Every picture was compared with 5 other pictures, with one of them containing a head of the same person. If I don't count the 4 pictures, where the algorithm wasn't able to detect the head and 2 pictures, which should have been assigned to these pictures. The algorithm successfully determined, that the head of the same person is the most similar on 18 pictures. On 5 pictures the head of the same person was determined as the 3rd most similar and on one of the pictures the algorithm failed completely and determined the head of the same person to be the least similar. The algorithm is successful with head comparison in different positions on most of the pictures.
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Algorithm for Head Comparison in Non-Standard Views
Wysoglad, Jaromír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
The goal of this work is to create an algorithm for human head comparison. The algorithm is able to compare heads in a lot of different positions, but the heads, that are being compared, must be in the same position. At first the algorithm uses some freely available detectors for detecting heads and head parts. Then a histogram of oriented gradients is computed for each part of each head and by comparing them the algorithm finds out the dissimilarity of the heads. From the testing set of 30 pictures the algorithm is able to successfully detect heads on 26 pictures. Every picture was compared with 5 other pictures, with one of them containing a head of the same person. If I don't count the 4 pictures, where the algorithm wasn't able to detect the head and 2 pictures, which should have been assigned to these pictures. The algorithm successfully determined, that the head of the same person is the most similar on 18 pictures. On 5 pictures the head of the same person was determined as the 3rd most similar and on one of the pictures the algorithm failed completely and determined the head of the same person to be the least similar. The algorithm is successful with head comparison in different positions on most of the pictures.
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Face Recognition
Duban, Michal ; Beran, Vítězslav (referee) ; Smrž, Pavel (advisor)
This Bachelor's thesis aimed to develop an application, executable by the robot PR2, capable of automatical recognizing and memorizing human faces which are displayed successively on video recorded by cameras placed on the robot.
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Emotional State Analysis Upon Image Patterns
Přinosil, Jiří ; Rajmic, Pavel (advisor)
This dissertation thesis deals with the automatic system for basic emotional facial expressions recognition from static images. Generally the system is divided into the three independent parts, which are linked together in some way. The first part deals with automatic face detection from color images. In this part they were proposed the face detector based on skin color and the methods for eyes and lips position localization from detected faces using color maps. A part of this is modified Viola-Jones face detector, which was even experimentally used for eyes detection. The both face detectors were tested on the Georgia Tech Face Database. Another part of the automatic system is features extraction process, which consists of two statistical methods and of one method based on image filtering using set of Gabor’s filters. For purposes of this thesis they were experimentally used some combinations of features extracted using these methods. The last part of the automatic system is mathematical classifier, which is represented by feed-forward neural network. The automatic system is utilized by adding an accurate particular facial features localization using active shape model. The whole automatic system was benchmarked on recognizing of basic emotional facial expressions using the Japanese Female Facial Expression database.
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