A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

The accuracy and security of a biometric system are the two sides of a coin. A biometric system must be simple, flexible, efficient, and secure enough from unauthorized access. Concerning these requirements, this article presents an enhanced palmprint recognition system with superior performance and improved biometric template protection schemes. This method comprises five components: image preprocessing, feature extraction, cancelable biometrics, classification, and Bio-Cryptosystem. The palm texture region is extracted from the input hand image during preprocessing. A feature representation technique is then used to extract discriminant features from the palmprint texture patterns. The extracted features are then analyzed using an information coding scheme to compute a user-specific token. Here, the user-specific tokens along with the original palmprint features are used to calculate the feature vectors for the cancelable biometric system. A novel Bio-Cryptosystem has been proposed to protect templates with high security where there is no need to remember the users’ tokens. Cancelable feature vectors are kept online in encrypted form using the proposed Bio-Cryptosystem, which is used to identify or verify the subjects after decryption. The original feature vectors are kept offline to prevent these from external attacks and misuse. Finally, the multiclass linear support vector machine has been used for user classification. For experimental purposes, two benchmark hand image databases: BOSPHORUS and CASIA-Palmprint, are employed, and the proposed system has achieved nearly 100% correct recognition rate in cancelable domain for both the employed databases. Finally, the performance comparison in terms of both verification and identification for the palmprint and the cancelable palmprint recognition systems with the existing state-of-the-art methods shows the superiority of the proposed system.