Scale Invariant Feature Transform（SIFT）algorithm is widely used for ear feature matching and recognition. However, the application of the algorithm is usually interfered by the non-target areas within the whole image, and the interference would then affect the matching and recognition of ear features. To solve this problem, a combined image segmentation algorithm i.e. KRM was introduced in this paper, As the human ear recognition pretreatment method. Firstly, the target areas of ears were extracted by the KRM algorithm and then SIFT algorithm could be applied to the detection and matching of features. The present KRM algorithm follows three steps: (1)the image was preliminarily segmented into foreground target area and background area by using K-means clustering algorithm; (2)Region growing method was used to merge the over-segmented areas; (3)Morphology erosion filtering method was applied to obtain the final segmented regions. The experiment results showed that the KRM method could effectively improve the accuracy and robustness of ear feature matching and recognition based on SIFT algorithm.

Date Published: 26 February 2014
PDF: 8 pages
Proc. SPIE 8942, Dynamics and Fluctuations in Biomedical Photonics XI, 89420Z (26 February 2014); doi: 10.1117/12.2036893

Published in SPIE Proceedings Vol. 8942:
Dynamics and Fluctuations in Biomedical Photonics XI
Valery V. Tuchin; Donald D. Duncan; Kirill V. Larin; Martin J. Leahy; Ruikang K. Wang, Editor(s)