Reducing the dimensionality of the original pattern space in a definition of feature space while maintaining discriminatory power for classification is a general goal in pattern recognition. To accomplish this goal in the area of ear biometrics a highly recognized work was proposed by D. Hurley in 2D space. We were inspired by his work and developed a new method for 3D data. In a different way to Hurley’s work we obtain a potential energy surface from 3D depth image which underlies the force field and associated vector field has its own characteristics. Our feature extraction is conducted by combining two different approaches; an algorithmic approach as well as an analytical approach, both are based on the vector force field and geometrical approach which is based on 3D ear surface. To validate the technique, the ICP algorithm is used. This work differs from Hurley’s work not only because of the algorithm, but also because of the nature of the 3D data which delivers topological information of the images. We exploit geometry to acquire surface information of the ear which yields richer features than the original work. The performance of the proposed method was evaluated using the University of Notre Dame (UND) collection J2 database and MATLAB has been used as the software package. / phone number: 0700099273