Biometric Bits Extraction Through Phase Quantization Based on Feature Level Fusion
Biometric bits extraction has emerged as an essential technique for the study of biometric template protection as well as biometric cryptosystems. In this paper, the authors present a non-invertible but revocable bits extraction technique by means of quantizing the facial data from two feature extractors in the phase domain, which they coin as Aligned Feature-level fusion Phase Quantization (AFPQ). In this technique, they utilize helper data to achieve the revocability requirement of bits extraction. The feature averaging and remainder normalization technique are integrated with the helper data to reduce feature variance within the same individual and increase the distinctiveness of bit strings of different individuals to achieve good recognition performance.