Ear Recognition Based on Forstner and SIFT - Ma Chi; Zhu Yongyong; Tian Ying (Indonesian Journal of Electrical Engineering and Computer Science, 2013)
Extraction and expression of features are critical to improving the recognition rate of ear image recognition. This paper proposes a new ear recognition method based on SIFT (Scale-invariant feature transform) and Forstner corner detection technology. Firstly, Forstner corner points and SIFT keypoints are detected respectively. Then taking Forstner corner into the SIFT algorithm to calculate their descriptor as the image feature vectors. Finally ear recognition based on these feature is carried out with Euclidean distance as similarity measurement. A bi-directional matching algorithm is utilized for improving recognition rate. Experiments on USTB database show that the recognition rate reaches more 94%. The Experimental results prove the effectiveness of the proposed method in term of recognition accuracy in comparison with previous methods. It is robust to rigid changes of ear image and provides a new approach to the research for ear recognition. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.2760