A Fuzzy Vault Scheme for Feature Fusion

Widespread application of biometric authentication brings about new problem of privacy. Biometric template protection is becoming a hot research. Efficient feature fusion is deemed to have good performance possibly. In this paper we proposed a fuzzy vault scheme for feature fusion. In our scheme, two facial features Multi-Block Local Binary Pattern (MB-LBP) and Principal Component Analysis (PCA) coefficients are extracted. A key is split into two overlapped subkeys. One is utilized to generate a set of helper data from MB-LBP. The other is utilized to generate another set of helper data from PCA coefficients. Two sets of helper data are submerged into the chaff points set and the final fuzzy vault is generated. In the fuzzy vault decoding, the MB-LBP and PCA coefficients of the query face image are utilized to recover two subkeys from the fuzzy vault. The final key is obtained from two subkeys. Because two subkeys are overlapped and complementary to each other, our scheme can obtain good authentication performance. It is confirmed by the experimental results.