Improving the Accuracy for Biometrics using External Auditory Canal

The use of biometrics authentication technology has become widespread such as the face recognition is increasingly being used in the airport, hospital. If a technology with the same level of accuracy and convenience is developed in the future, it is expected to be used in a variety of fields. The purpose of this study is to improve the convenience of biometrics authentication. We are focusing on external auditory canal, which is less susceptible to the effects of the outside environment. We are conducting research and development into a personal authentication system using images of external auditory canal, and our findings underscore the fact that have the features of individual differences inside the ear canal. In the proposed method, images taken using a light source were processed to artificially enhance the red color, and the accuracy of personal identification using VGG16 was evaluated on images of both ears of 13 people. Specifically, as a preprocessing step, we created a thermography-like image from the original image and extracted the red regions from it. Using a trained model with processed image data, we evaluated the accuracy of classification, and the accuracy improved from 0.989 to 0.999. The results of this study suggest that slightly higher accuracy can be achieved than with conventional methods. The multiple image data were extracted from video data in the ear canal, and the images were classified using the representative CNN algorithm VGG16, and it was confirmed that a high level of accuracy could be achieved. In the future, we plan to verify the shortening of the learning time.