A Binarization Method for Extracting High Entropy String in Gait Biometric Cryptosystem

Inertial-sensors based gait has been considered as a promising approach for user authentication in mobile devices. However, securing enrolled template in such system remains a challenging task. Biometric Cryptosystems (BCS) provide elegant approaches for this matter. The primary task of adopting BCS is to extract from raw biometric data a discriminative, high entropy and stable binary string, which will be used as input of BCS. Unfortunately, the state-of-the-art researches does not notice the gait features' population distribution when extracting such string. Thus, the extracted binary string has low entropy, and degrades the overall system security. In this study, we address the aforementioned drawback to improve entropy of the extracted string, and also enhance the system security. Specifically, we design a binarization scheme, in which the distribution population of gait features are analyzed and utilized to allow the extracted binary string achieving maximal entropy. In addition, the binarization is also designed to provide strong variation toleration to produce highly stable binary string which enhances the system friendliness. We analyzed the proposed method using a gait dataset of 38 volunteers which were collected under nearly realistic conditions. The experiment results show that our proposed binarization method improves the extracted binary string's entropy 30%, and the system achieved competitive performance (i.e., 0.01% FAR, 9.5% FRR with 139-bit key).