Fingerprint Recognition with Edge Detection and Dimensionality Reduction Techniques
Ratiporn Chanklan, Kedkarn Chaiyakhan, Anusara Hirunyawanakul, Kittisak Kerdprasop, Nittaya Kerdprasop · 2015
At present fingerprint recognition has been used widely, such as an authentication means of mobile phone usage and a monitoring for working hours. But the recognition performance of existing system low. We thus propose techniques to improve the recognition. We notice that edge detection techniques applied to the fingerprint images can enhance the quality of images and cause the improvement in image recognition We thus study the four edges detection techniques: sobel, prewitt, robert and canny. For faster classification we also apply two dimensionality reduction techniques: principal component analysis and linear discriminant analysis. Then, we identify fingerprint images with the algorithm support vector machine using linear kernel function. Experimental results showed that the pre-processing fingerprint images using canny edge detection with principal component analysis can increased the recognition rate from 64.3% to 88%. On using canny edge detection with linear discriminant analysis, the fingerprint image recognition can be improved from 73.8% to 88%