Ear recognition based on force field feature extraction and convergence feature extraction

Ear recognition based on the force field transform is new and effective. Three different applications of the force field transform were discussed in this paper. Firstly, we discussed the problem in the process of potential wells extraction and overcame the contradiction between the continuity of the force field and the discreteness of intensity images. Secondly, an improved convergence-based ear recognition method was presented in this paper. To overcome the problem of threshold segmentation, an adaptive threshold segmentation method was used to find the threshold automatically; to reduce the computational complexity, a quick classification was realized by combining the Canny-operator and the Modified Hausdorff Distance (MHD). Finally, the algebraic property of force field was combined with Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) together to obtain feature vectors for ear recognition. We tested these applications of the force field transform on two ear databases. Experimental results show the validity and robustness of the force field transform for ear recognition.