Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

Rotation-Invariant Neoperceptron

B. Fasel and D. Gatica-Perez
International Conference on Pattern Recognition (ICPR 2006)
Hong Kong, China, August 2006

Abstract

Approaches based on local features and descriptors are increasingly used for the task of object recognition due to their robustness with regard to occlusions and geometrical deformations of objects. In this paper we present a local feature based, rotation-invariant Neoperceptron. By extending the weight-sharing properties of convolutional neural networks to orientations, we obtain a neural network that is inherently robust to object rotations, while still being capable to learn optimally discriminant features from training data. The performance of the network is evaluated on a facial expression database and compared to a standard Neoperceptron as well as to the Scale Invariant Feature Transform (SIFT), a-state-of-the-art local descriptor. The results confirm the validity of our approach.


Download in pdf format
@InProceedings{eth_biwi_00443,
  author = {B. Fasel and D. Gatica-Perez},
  title = {Rotation-Invariant Neoperceptron},
  booktitle = {International Conference on Pattern Recognition (ICPR 2006)},
  year = {2006},
  month = {August},
  keywords = {Neoperceptron, SIFT, Local Features, Facial Expression Recognition, Object Recognition}
}