Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài :An analysis on equal width quantization and linearly separable subcode encoding-based discretization and its performance resemblances | Lim et al. EURASIP Journal on Advances in Signal Processing 2011 2011 82 http content 2011 1 82 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access An analysis on equal width quantization and linearly separable subcode encoding-based discretization and its performance resemblances Meng-Hui Lim Andrew Beng Jin Teoh and Kar-Ann Toh Abstract Biometric discretization extracts a binary string from a set of real-valued features per user. This representative string can be used as a cryptographic key in many security applications upon error correction. Discretization performance should not degrade from the actual continuous features-based classification performance significantly. However numerous discretization approaches based on ineffective encoding schemes have been put forward. Therefore the correlation between such discretization and classification has never been made clear. In this article we aim to bridge the gap between continuous and Hamming domains and provide a revelation upon how discretization based on equal-width quantization and linearly separable subcode encoding could affect the classification performance in the Hamming domain. We further illustrate how such discretization can be applied in order to obtain a highly resembled classification performance under the general Lp distance and the inner product metrics. Finally empirical studies conducted on two benchmark face datasets vindicate our analysis results. 1. Introduction Explosion of biometric-based cryptographic applications see . 1-12 in the recent decade has abruptly augmented the demand of stable binary strings for identity representation. Biometric features extracted by most current feature extractors however do not exist in binary form by nature. In the case where binary processing is needed biometric discretization becomes necessary in order to transform such an ordered set of continuous features into a binary string. Note that
