TY - GEN
T1 - Rotation invariant face detection using convolutional neural networks
AU - Tivive, Fok Hing Chi
AU - Bouzerdoum, Abdesselam
PY - 2006
Y1 - 2006
N2 - This article addresses the problem of rotation invariant face detection using convolutional neural networks. Recently, we developed a new class of convolutional neural networks for visual pattern recognition. These networks have a simple network architecture and use shunting inhibitory neurons as the basic computing elements for feature extraction. Three networks with different connection schemes have been developed for in-plane rotation invariant face detection: fully-connected, toeplitz-connected, and binary-connected networks. The three networks are trained using a variant of Levenberg-Marquardt algorithm and tested on a set of 40,000 rotated face patterns. As a face/non-face classifier, these networks achieve 97.3% classification accuracy for a rotation angle in the range ±90° and 95.9% for full in-plane rotation. The proposed networks have fewer free parameters and better generalization ability than the feedforward neural networks, and outperform the conventional convolutional neural networks.
AB - This article addresses the problem of rotation invariant face detection using convolutional neural networks. Recently, we developed a new class of convolutional neural networks for visual pattern recognition. These networks have a simple network architecture and use shunting inhibitory neurons as the basic computing elements for feature extraction. Three networks with different connection schemes have been developed for in-plane rotation invariant face detection: fully-connected, toeplitz-connected, and binary-connected networks. The three networks are trained using a variant of Levenberg-Marquardt algorithm and tested on a set of 40,000 rotated face patterns. As a face/non-face classifier, these networks achieve 97.3% classification accuracy for a rotation angle in the range ±90° and 95.9% for full in-plane rotation. The proposed networks have fewer free parameters and better generalization ability than the feedforward neural networks, and outperform the conventional convolutional neural networks.
UR - http://www.scopus.com/inward/record.url?scp=33750684980&partnerID=8YFLogxK
U2 - 10.1007/11893257_29
DO - 10.1007/11893257_29
M3 - Conference contribution
AN - SCOPUS:33750684980
SN - 3540464816
SN - 9783540464815
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 260
EP - 269
BT - Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PB - Springer Verlag
T2 - 13th International Conference on Neural Information Processing, ICONIP 2006
Y2 - 3 October 2006 through 6 October 2006
ER -