A target area detection method for improving ear feature point recognition degree

SURF (speeded up robust features) algorithm is widely used for ear feature matching and recognition. However, the application of the algorithm is usually interfered by non-target areas within the whole image, and the interference would affect the matching and recognition accuracy of ear features. Ear feature recognition algorithm based on the target area can highlight the impact of target area, and suppress the influence of the background area as much as possible. To solve this problem, a combined image segmentation algorithm, i.e. KRM, was introduced as a preprocessing method in this paper to extract the target area from the image. 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 combination of KRM and SURF (KRM-SURF algorithm) was employed to detect and match feature points in 50 sets of ear images, achieving recognition degree (RD) of up to 0.924. Results show that, based on SURF algorithm, the KRM algorithm can effectively improve the accuracy of ear feature matching and recognition.