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01 Nov 2014
TL;DR: A convolution of Gabor filter with frequency domain of training and test images provided a feature vector that was sourced to neural network of 2 hidden layer with scaled conjugate training.
Abstract: This paper proposes a method for detecting faces in a given image by combining Gabor filter and Neural network. The first phase uses gabor filter which generates a fetaure set. Face and non face templates is taken and processed with gabor filter.The face images are present in spatial (time) domain. The conversion of images into frequency domain is processed through inverse fast fourier transform. The subsequent frquency domain images is conjugated with gabor filter bank and feature vector is generated. The second phase involves a method where all the features are given as input to neural network of 2 hidden layer with scaled conjugate training. Thus this approach being deployed, is a convolution of Gabor filter with frequency domain of training and test images provided a fetaure vector that was sourced to neural network. Proposed system was tested and the results indicated the efficient performance.
Authors
Showing all 350 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Narasimha H. Ayachit | 15 | 104 | 703 |
| Arjumand A. Kittur | 14 | 17 | 807 |
| S. C. Shiralashetti | 13 | 45 | 493 |
| Varsha S. Joshi | 11 | 17 | 405 |
| A.A. Kittur | 11 | 12 | 673 |
| V.S. Yaliwal | 10 | 35 | 368 |
| Umakant P. Kulkarni | 10 | 65 | 372 |
| S. R. Biradar | 10 | 38 | 330 |
| Suresh Chavhan | 9 | 26 | 169 |
| Mrityunjaya V. Latte | 9 | 38 | 214 |
| P. S. Shivakumar Gouda | 8 | 29 | 206 |
| M.N. Kalasad | 8 | 9 | 212 |
| Satish S. Bhairannawar | 6 | 19 | 80 |
| G S Thyagaraju | 6 | 12 | 80 |
| V. S. Hegde | 6 | 11 | 107 |