Innovative Hetero-Associative Memory Encoder (HAMTE) for Palmprint Template Protection.

Many types of research focus on utilizing Palmprint recognition in user
identification and authentication. The Palmprint is one of biometric authentication
(something you are) invariable during a person’s life and needs careful protection
during enrollment into different biometric authentication systems. Accuracy and
irreversibility are critical requirements for securing the Palmprint template during
enrollment and verification. This paper proposes an innovative HAMTE neural
network model that contains Hetero-Associative Memory for Palmprint template
translation and projection using matrix multiplication and dot product multiplication. A HAMTE-Siamese network is constructed, which accepts two Palmprint
templates and predicts whether these two templates belong to the same user or
different users. The HAMTE is generated for each user during the enrollment
phase, which is responsible for generating a secure template for the enrolled user.
The proposed network secures the person’s Palmprint template by translating it
into an irreversible template (different features space). It can be stored safely in
a trusted/untrusted third-party authentication system that protects the original person’s template from being stolen. Experimental results are conducted on the
CASIA database, where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates. The recognition accuracy
deviated by around 3%, and the equal error rate (EER) by approximately 0.02 compared to the original data, with appropriate performance (approximately 13 ms)
while preserving the irreversibility property of the secure template. Moreover, the
brute-force attack has been analyzed under the new Palmprint protection scheme.