Application of the Polar-Fourier Greyscale Descriptor to the Problem of Identification of Persons Based on Ear Images

The threat caused by criminality and terrorism resulted in a significant increase in the interest in research focused on effective methods for its reducing. One of the most important solutions for the mentioned problem is the biometric identification of persons. Hence, it became a very important issue nowadays. A crucial aspect of biometrics is the searching for automatic methods that may be applied for the recognition of human beings. Currently, fingerprints as well as the face are the most popular applied biometric features. However, in order to increase the efficiency of the developed systems, new modalities are becoming more and more popular. On one hand, researchers are looking for more effective biometrics while on the other hand the idea of applying few modalities jointly in multimodal systems is becoming more popular. An ear is an example of lately explored new biometric features. Its uniqueness is the most important advantage. Similarly to the face, the auricle is distinguishable for various persons, thanks to its complex and stable structure. Therefore, in the paper an algorithm for human identification based on ear images is presented and tested. It uses the improved version of the Polar-Fourier Greyscale Descriptor for feature representation. The method was tested using 225 digital ear images for 45 persons and has achieved 84% efficiency.

[1]  Th. M. Hupkens,et al.  Noise and intensity invariant moments , 1995, Pattern Recognit. Lett..

[2]  B. Moreno,et al.  On the use of outer ear images for personal identification in security applications , 1999, Proceedings IEEE 33rd Annual 1999 International Carnahan Conference on Security Technology (Cat. No.99CH36303).

[3]  Hui Chen,et al.  Human Ear Recognition in 3D , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Mark S. Nixon,et al.  Force field energy functionals for image feature extraction , 2002, Image Vis. Comput..

[5]  Anton H. M. Akkermans,et al.  Acoustic Ear Recognition , 2006, ICB.

[6]  Mark S. Nixon,et al.  Automatic ear recognition by force field transformations , 2000 .

[7]  Stan Z. Li,et al.  Advances in Biometrics, International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007, Proceedings , 2007, ICB.

[8]  Zhengguang Xu,et al.  Shape and Structural Feature Based Ear Recognition , 2004, SINOBIOMETRICS.

[9]  Dariusz Frejlichowski.  Identification of Erythrocyte Types in Greyscale MGG Images for Computer-Assisted Diagnosis , 2011, IbPRIA.

[10]  Sudeep Sarkar,et al.  Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Kevin W. Bowyer,et al.  ICP-based approaches for 3D ear recognition , 2005, SPIE Defense + Commercial Sensing.

[12]  Anil K. Jain,et al.  Biometric technology for human identification , 2004 .

[13]  Ping Yan,et al.  A fast algorithm for ICP-based 3D shape biometrics , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[14]  Sharath Pankanti,et al.  Advances in Biometric Person Authentication, International Workshop on Biometric Recognition Systems, IWBRS2005, Beijing, China, October 22-23, 2005, Proceedings , 2005, IWBRS.