Gabor-Based RCM Features for Ear Recognition

Ear biometrics has received deficient attention compared to the more popular techniques of face, eye, or fingerprint recognition. The ear as a biometric is no longer in its infancy and it has shown encouraging progress so far. ears have played an important role in forensic science for many years, especially in the United States, where an ear classification system based on manual measurements was developed by (Iannarelli, 1989). In recent years, biometrics recognition technology has been widely investigated and developed. Human ear, as a new biometric, not only extends existing biometrics, but also has its own characteristics which are different from others. Iannarelli has shown that human ear is one of the representative human biometrics with uniqueness and stability (Iannarelli, 1989). Since ear as a major feature for human identification was firstly measured in 1890 by Alphonse Bertillon, so-called ear prints have been used in the forensic science for a long time (Bertillon, 1890). Ears have certain advantages over the more established biometrics; as Bertillon pointed out, they have a rich and stable structure that does not suffer from the changes of ages, skin-color, cosmetics, and hairstyles. Also the ear does not suffer from changes in facial expression, and is firmly fixed in the middle of the side of the head so that the background is more predictable than is the case for face recognition which usually requires the face to be captured against a controlled background. The ear is large compared with the iris, retina, and fingerprint and therefore is more easily captured at a distance. We presented gabor-based region covariance matrix as an efficient feature for ear recognition. In this method, we construct a region covariance matrix by using gabor features, illumination intensity component, and pixel location, and use it as an efficient and robust ear descriptor for recognizing peoples. The feasibility of the proposed method has been successfully tested on ear recognition using two USTB databases, specifically used total 488 ear images corresponding to 137 persons. The effectiveness of the proposed method is shown in terms of the comparative performance against some popular ear recognition methods. This chapter is organized as follows. In section 2, related works are presented. In section 3, region covariance matrix (RCM) and the method for fast RCM computation are presented. In section 4, the proposed method presented in detail. In section 5, ear image databases are introduced. In section 6, experimental results are shown and commented. The chapter concludes in section 7.