Cancelable biometric scheme based on dynamic salting of random patches

The idea of cancelable biometrics was proposed to ensure the privacy and security of user’s original biometric data, which is collected for recognition by storing only transformed template in the database after applying irreversible operations. State of the art cancelable schemes use either biometric salting or non-linear transformations for generating protected template. However, biometric salting techniques suffer from the threat of unauthorized access to user specific key; whereas non-linear transformation based schemes have the inherent issue of performance degradation due to information loss. In order to overcome these limitations of the existing techniques, this work proposes a patch level distortion based cancelable scheme which can be applied for multiple modalities. In the proposed method, biometric images are divided into Voronoi patches and log Gabor feature vector of each patch is distorted by combining it with a function of difference vectors of other patches. Proposed scheme is able to eliminate the need of security critical user specific key and many to one transformations. Irreversibility is assured as the function of difference vectors of patches used as noise in distortion process is random enough and unpredictable for an intruder. Cancelability and non-linkability can be ensured by changing the number and location of seed points. Superior performance compared to state of the art techniques is obtained in distorted domain when experiments were conducted on CASIA face database.