Gaithashing: A two-factor authentication scheme based on gait features

Recently, gait recognition has attracted much attention as a biometric feature for real-time person authentication. The main advantage of gait is that it can be observed at a distance in an unobtrusive manner. However, the security of an authentication system, based only on gait features, can be easily broken. A malicious actor can observe the gait of an unsuspicious person and extract the related biometric template in a trivial manner and without being noticed. Another major issue of gait as an identifier has to do with their high intra-variance, since human silhouettes can be significantly modified, when for example the user holds a bag or wears a coat. This paper proposes gaithashing, a two-factor authentication that interpolates between the security features of biohash and the recognition capabilities of gait features to provide a high accuracy and secure authentication system. A novel characteristic of gaithashing is that it enrolls three different human silhouettes types. During authentication, the new extracted gait features and the enrollment ones are fused using weighted sums. By selecting appropriate weight values, the proposed scheme eliminates the noise and distortions caused by different silhouette types and achieves to authenticate a user independently of his/her silhouette. Apart from high accuracy, the proposed scheme provides revocability in case of a biometric template compromise. The performance of the proposed scheme is evaluated by carrying out a comprehensive set of experiments. Numerical results show that gaithashing outperforms existing solutions in terms of authentication performance, while at the same time achieves to secure the gait features.