Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face recognition using 2D and disparity eigenface
Expert Systems with Applications: An International Journal
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized regression neural networks in time-varying environment
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper presents a procedure for face detection and recognition. Then face detection is done based on the theory of high correlation of face images at low resolutions. The face detected is called the mug shot. Generalized Regression based Neural Networks is used for training the mug shot to represent eyes in the form of rectangles. GRNN gives good results to represent eyes in the form of rectangles. Eye coordinates are calculated and straight line distance between the centers of two eyes is calculated. This calculated distance comes out to be different for different face images. Face recognition is done by comparing the calculated distance of source face with the target face. The system developed by this approach is suitable for finding face images from a set of given images. This algorithm can be implemented for searching and finding people from a database on a computer. However Automatic Face Recognition is a complicated problem due to the variability of face expressions, face positions and lighting conditions. Many methods have been proposed for face recognition but their performance and precision is still very far as compared to that of the human performance.