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Ear recognition is a new technology and future tren d for personal identification. However, the false d etection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Po int (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a good steadiness to reduce the false de tection rate. The corresponding ear extracts from the side range image and characterized by 3D features. The proposed method used matlab simulation and defined the average detection time 35ms and an identification simila rity is 98.25% for the collection of different database. Th e result shows that the proposed combined method outperforms than the existing of ICP or SCM in terms of detection time and accuracy in training.

3-DIMENSIONAL EAR RECOGNITION BASED ITERATIVE CLOSEST POINT WITH STOCHASTIC CLUSTERING MATCHING