A De-Identification Face Recognition Using Extracted Thermal Features Based on Deep Learning
Chih‐Hsueh Lin, Zhi-Hao Wang, Gwo-Jia Jong
Abstract
Facial recognition based on visible-light RGB image is a mature technique. With the continuous progress of civilization, the protection against personal privacy and the effectiveness of the system is more stringent. In the past research, the distance from facial features was used as the de-identified biological feature, but this could not solve the problem of using a photo to deceive the system. Therefore, we used thermal image to convert into features by using the proposed feature extraction method, and uses deep learning, random forest, and ensemble learning to build a face prediction model. The proposed feature extraction method cuts the facial image (RGB and thermal image) into 12 and 48 blocks respectively as well as regenerates the feature image and the feature matrix. Based on the experimental results, accuracy of RGB image only with 0.834, feature image with 0.953, and feature matrix with 0.967. The feature images and feature matrices produced by the proposed feature extraction method can achieve better prediction performance. High accuracy of thermal imaging can solve the problem of fake photos and the issue of personal privacy de-identification could be solved.