Genetic algorithm based local and global spectral features extraction for ear recognition.

• This paper proposes a new system for ear biometric identification. • It selects the best combination of local and global spectral features using GA. • The global features are extracted by applying the Gabor-Zernike operator. • The local phase quantization operator is used for extracting local features. Identification systems based on biometric features are becoming increasingly important. One of the most common biometric features is the ear. The accuracy of these systems is heavily dependent on the characteristics extracted from them. In this paper, an appropriate combination of local and global features in the frequency domain is extracted as unique features of the ear region. In the proposed approach, at first the image quality is improved by Contrast-limited Adaptive Histogram Equalization. Then, the global features of the ear region are extracted by applying the Gabor-Zernike operator to the whole image and its non-overlapping blocks. In addition, to extract of local features, the local phase quantization operator is used on the original image of the ear region. Then, the optimum combination of global and local features is selected using Genetic Algorithm. Finally, the nearest neighbor classifier with Canberra distance is used to identify users. The proposed approach is evaluated using three databases, i.e. USTB-1, IIT125 and IIT221. The recognition rate of 100%, 99.2% and 97.13%, is reported on these databases, respectively. The obtained results show that the proposed approach performs better than existing ear recognition methods. [ABSTRACT FROM AUTHOR]