Principal Component Analysis for Symmetric Key Generation

This work presents a novel biometric encryption scheme based on feature vectors extracted from a face recognition system. This system uses principal component analysis, in order to generate a symmetric secret key, being this key used to encrypt any information data, like a biometric template. The data is therefore concealed and only an individual having a similar biometric feature vector is capable to regenerate the correct key. This scheme is applied to a system using eigenfaces for recognition, where the corrected detected class from a sample image can guarantee the corrected generation of a symmetric key. Due to the efficiency of the system being dependent of the face recognition algorithm, the tests showed a rate of 90.4% of corrected symmetric key generation, or sucessfull encryption/ decryption scheme, for 25 face classes, with 5 images each.