On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and natural intelligence
Neural networks and natural intelligence
Moment-Based Image Normalization With High Noise-Tolerance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Backpropagation applied to handwritten zip code recognition
Neural Computation
Two highly efficient second-order algorithms for training feedforward networks
IEEE Transactions on Neural Networks
Efficient training algorithms for a class of shunting inhibitory convolutional neural networks
IEEE Transactions on Neural Networks
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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 ±900 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.