Abstract
Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression , and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance , and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located , and a set of shape , and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition , and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting , and facial expression. The system performs well on all the tasks listed above. © 1997 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 743-756 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 19 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1997 |
Keywords
- Deformable templates
- Expression recognition
- Face recognition
- Facial feature location
- Pose recovery, coding-reconstruction