Ear Recognition Using Multi-Scale Histogram of Oriented Gradients

Ear recognition is a promising biometric measure, especially with the growing interest in multi-modal biometrics. Histogram of Oriented Gradients (HOG) have been effectively and efficiently used solving the problems of object detection and recognition, especially when illumination variations are present. This work presents a robust approach for ear recognition using multi-scale dense HOG features as a descriptor of 2D ear images. The multi-scale features assure to capture the different and complicated structures of ear images. Dimensionality reduction was performed to avoid feature redundancy and provide a more efficient recognition process while being prone to over-fitting. Finally, a test was performed on a large and realistic database and the results were compared to the state of the art ear recognition approaches tested on the same dataset and under the same test procedure.