Name
Abstract | ||
|---|---|---|
One of the major difficulties in face recognition systems is the in-depth pose variation problem. Most face recognition approaches assume that the pose of the face is known. In this work, we have designed a feature based pose estimation and face recognition system using 2D Gabor wavelets as local feature information. The difference of our system from the existing ones lies in its simplicity and its intelligent sampling of local features. Intelligent feature selection can be carried out by learning a set of parameters where the aim is the optimal performance of the overall system. In this paper, we give comparative analysis of the performance of our system with the standard modular Eigenfaces approach and show that local feature based approach improved the performance of both pose estimation and face recognition. For efficient coding, we have employed Principal Component Analysis (PCA) to the outputs of local feature vectors. Intelligent feature selection also reduced the space and time complexity of the system while retaining almost the same estimation and recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICPR.2002.1047457 | Pattern Recognition, 2002. Proceedings. 16th International Conference |
Keywords | Field | DocType |
face recognition,feature extraction,image coding,image sampling,learning (artificial intelligence),principal component analysis,wavelet transforms,2D Gabor wavelets,efficient coding,estimation accuracy,feature selection,in-depth pose variation problem,intelligent feature selection,intelligent local feature sampling,local feature information,modular eigenfaces approach,optimal performance,parameter set learning,pose estimation,pose invariant face recognition,principal component analysis,recognition accuracy,space complexity,time complexity | Facial recognition system,Computer vision,Feature vector,Eigenface,Three-dimensional face recognition,Pattern recognition,Feature (computer vision),Computer science,3D pose estimation,Feature extraction,Feature (machine learning),Artificial intelligence | Conference |
Volume | ISSN | ISBN |
4 | 1051-4651 | 0-7695-1695-X |
Citations | PageRank | References |
14 | 0.82 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berk Gokberk | 1 | 113 | 6.23 |
| lale akarun | 2 | 1201 | 70.68 |
| Ethem Alpaydin | 3 | 858 | 90.05 |