Name
Abstract | ||
|---|---|---|
Radial basis function (RBF) networks are compared with other neural network techniques on a face recognition task for applications involving identification of individuals using low-resolution video information. The RBF networks are shown to exhibit useful shift, scale and pose (y-axis head rotation) invariance after training when the input representation is made to mimic the receptive field functions found in early stages of the human vision system. In particular, representations based on difference of Gaussian (DoG) filtering and Gabor wavelet analysis are compared. Extensions of the techniques to the case of image sequence analysis are described and a time delay (TD) RBF network is used for recognising simple movement-based gestures. Finally, we discuss how these techniques can be used in real-life applications that require recognition of faces and gestures using low-resolution video images. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1016/S0925-2312(98)00016-2 | Neurocomputing |
Keywords | Field | DocType |
Face recognition,Invariance,Time-delay networks,Receptive field functions,Image sequences | Radial basis function,Machine vision,Gabor wavelet,Gesture,Artificial intelligence,Artificial neural network,Facial recognition system,Computer vision,Pattern recognition,Hierarchical RBF,Mathematics,Machine learning,Difference of Gaussians | Journal |
Volume | Issue | ISSN |
20 | 1-3 | 0925-2312 |
Citations | PageRank | References |
20 | 1.22 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jon howell | 1 | 585 | 39.63 |
| Hilary Buxton | 2 | 491 | 135.93 |