Gabor Filter and Canny Edge Detection for Ear Biometrics Identification

Ear biometrics, the use of the unique physical characteristics of the ear for identification purposes, has gained increasing attention in recent years due to its high level of accuracy and stability. However, extracting the biometric features of the ear from images can be challenging due to variations in ear shape and size across different populations. This study proposes a new approach for ear biometric identification using Gabor filters and Canny edge detection. Gabor filters are a type of wavelet that can be used to extract texture and orientation information from images, while Canny edge detection is a widely used edge detection algorithm that can accurately identify object boundaries. Our experiments on a small dataset of ear images show that our proposed method gives great results of accuracy and robustness. The results of our study demonstrate the potential of Gabor filters and Canny edge detection for improving the performance of ear biometric systems.