대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)
- 대한전자공학회 2002년도 ITC-CSCC -2
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- Pages.814-817
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- 2002
Interpolation on data with multiple attributes by a neural network
- Azumi, Hiroshi (Graduate School of Science and Engineering, Saitama University) ;
- Hiraoka, Kazuyuki (Department of Infromation and Computer Science, Saitama University) ;
- Mishima, Taketoshi (Department of Infromation and Computer Science, Saitama University)
- 발행 : 2002.07.01
초록
High-dimensional data with two or more attributes are considered. A typical example of such data is face images of various individuals and expressions. In these cases, collecting a complete data set is often difficult since the number of combinations can be large. In the present study, we propose a method to interpolate data of missing combinations from other data. If this becomes possible, robust recognition of multiple attributes is expectable. The key of this subject is appropriate extraction of the similarity that the face images of same individual or same expression have. Bilinear model [1]has been proposed as a solution of this subjcet. However, experiments on application of bilinear model to classification of face images resulted in low performance [2]. In order to overcome the limit of bilinear model, in this research, a nonlinear model on a neural network is adopted and usefulness of this model is experimentally confirmed.
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