OKAMOTO Noriyoshi KANTOGAKUIN UNIV., Dept.of ElectriC Eng., Associate Professor, 工学部, 助教授 (10177090)
SHINOHARA Katsuyuki KOGAKUIN UNIV., Dept.of Electronic Eng., Associate Professor, 工学部, 助教授 (40100309)
NAKAMURA Osamu KOGAKUIN UNIV., Dept.of Electronic Eng., Professor, 工学部, 教授 (70100336)
|Budget Amount *help
¥6,600,000 (Direct Cost : ¥6,600,000)
Fiscal Year 1994 : ¥2,700,000 (Direct Cost : ¥2,700,000)
Fiscal Year 1993 : ¥3,900,000 (Direct Cost : ¥3,900,000)
Methods of desciption and classification of facial expression based on isodensity maps are investigated. Until now, almost all methods of recognition of facial expression have depended on the shapes and edges on facial elements (position of eyebrows, eyes, nose, mouth, etc.). However it is very difficult to extract such feature points and to describe the change of density variations such as wrinkles by using feature points. In contrast to these methods, this study adopts and defines isodensity maps that can represent density variation of facial images.
In this method, isodensity map is described as a tree structure. As isodensity map changes appear as changes in the tree structure, facial expression changes can be detected by analyzing the changes in the tree structure for example, by Brother Splitting, Brother Merging, Father-Son Splitting, or Father-Son Merging.
The processes in this method are as follows :
1) Extraction of an isodensity map from a facial image
2) Representaion of the tr
ee structure from an isodensity map
3) Detection of facial expression shanges based on the changes in the tree structure
A description and comparison method for extracting structure changes of tree is investigated and its efficacy is demonstrated experimentally. From experimental results, it is clear that the characteristic changes of facial expression can be extracted. These changes have been found in smiling and angry faces, because of large change in the density variation such as wrinkles. It is, however, difficult to find the characteristic change about surprise and sad faces which have little influence on wrinkles. As the tree matching proposed in this study is a method based on the position of isodensity line, it is difficult to find corresponding method between nodes representing small isodensity lines.
At the next stage, tree matching algorithm which can detect small change of facial expression must be developed.