Head-Pose Invariant Facial Expression Recognition Using Convolutional Neural Networks
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
3-D object recognition using 2-d poses processed by CNNs and a GRNN
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Evaluation of convolutional neural networks for visual recognition
IEEE Transactions on Neural Networks
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Automatic pattern recognition is a very important task in many applications such as image segmentation, object detection, etc. This work aims to find a new approach to automatically recognize patterns such as 3D objects and handwritten digits based on a database using General Regression Neural Networks (GRNN). The designed system can be used for both 3D object recognition from 2D poses of the object and handwritten digit recognition applications. The system does not require any preprocessing and feature extraction stage before the recognition. Simulation results show that pattern recognition by GRNN improves the recognition rate considerably in comparison to other neural network structures and has shown better recognition rates and much faster training times than that of Radial Basis Function and Multilayer Perceptron networks for the same applications.