원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.1
2014.02
pp.225-236
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, face recognition using the most representative SIFT images is presented. It is based on obtaining the SIFT (SCALE INVARIANT FEATURE TRANSFORM) features in different regions of each training image. Those regions were obtained using the K-means clustering algorithm applied on the key-points obtained from the SIFT algorithm. Based on these features, an algorithm which will get the most representative images of each face is presented. In the test phase, an unknown face image is recognized according to those representative images. In order to show its effectiveness this algorithm is compared to other SIFT algorithms and to the LDP algorithm for different databases.
목차
Abstract
1. Introduction
2. Scale Invariant Feature Tr
2.1. Construction a Scale Space
2.2. Laplacian of Gaussian Calculation
2.3. Finding Key-points
2.4. Eliminating Edges and Low Contrast Regions
2.5. Assigning an Orientation to the Key-points
2.6. SIFT Features Generation
3. Training Phase
3.1. The k-regions Formation
3.2. Obtaining the most Representative Images
4. Test Phase
5. Experimental Results
6. Conclusion
References
1. Introduction
2. Scale Invariant Feature Tr
2.1. Construction a Scale Space
2.2. Laplacian of Gaussian Calculation
2.3. Finding Key-points
2.4. Eliminating Edges and Low Contrast Regions
2.5. Assigning an Orientation to the Key-points
2.6. SIFT Features Generation
3. Training Phase
3.1. The k-regions Formation
3.2. Obtaining the most Representative Images
4. Test Phase
5. Experimental Results
6. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보