Binary Biometric Representation through Pairwise Polar Quantization

Binary biometric representations have great significance for data compression and template protection. In this paper, we introduce pairwise polar quantization. Furthermore, aiming to optimize the discrimination between the genuine Hamming distance (GHD) and the imposter Hamming distance (IHD), we propose two feature pairing strategies: the long-short (LS) strategy for phase quantization, as well as the long-long (LL) strategy for magnitude quantization. Experimental results for the FRGC face database and the FVC2000 fingerprint database show that phase bits provide reasonably good performance, whereas magnitude bits obtain poor performance.