Two- and three-dimensional patterns of the face
Two- and three-dimensional patterns of the face
Intelligent biometric techniques in fingerprint and face recognition
Intelligent biometric techniques in fingerprint and face recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
3D Human Face Recognition Using Point Signature
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Cascade Architectures of Fuzzy Neural Networks
Fuzzy Optimization and Decision Making
Journal of Cognitive Neuroscience
Surface Curvature as a Measure of Image Texture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. These surface curvature and eigenface, which reduce the data dimensions with less degradation of original information, are collaborated into the proposed 3D face recognition algorithm. The principal components represent the local facial characteristics without loss for the information. Recognition for the eigenface referred from the maximum and minimum curvatures is performed. The normalized facial images are also considered to enhance the recognition rate. To classify the faces, the cascade architectures of fuzzy neural networks, which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. Experimental results on a 46 person data set of 3D images demonstrate the effectiveness of the proposed method.