Nose pore recognition based on discriminant locality preserving projections
Shangling Song, Kazuhiko Ohnuma, Zhi Liu, Liangmo Mei · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
In this paper, we present a new member of the biometrics family, i.e. nose pores, based on DLPP. Little work has been done on nose pores as a biometric identifier. In this work, we made use of a database of nose pore images obtained over a long period to examine the performance of nose pores as a biometric identifier. First, the midpoint and midline were located and taken as reference for the ROI segmentation after nose image was segmented. Second, nose pore feature was filtered by LOG filters. Third, the extracted pore was projected to low dimensional space by DLPP. Finally, the feature in low dimension was classified by Euclidean distance. This research showed that the nose pore is a promising candidate for biometric identification and deserves further research. The experimental results based on the unique nose pores database demonstrated that nose pores can give a 91.91% correct recognition rate for biometric identification, which showed this biometric identifier’s feasibility and effectiveness. Compared with result without using DLPP, the feature extraction by DLPP was more precise.