By Haizhou Li, Kar-Ann Toh, Liyuan Li
Biometrics is the examine of equipment for uniquely spotting people in accordance with a number of intrinsic actual or behavioral characteristics. After many years of analysis actions, biometrics, as a well-known clinical self-discipline, has complex significantly either in sensible know-how and theoretical discovery to satisfy the expanding desire of biometric deployments. during this booklet, the editors offer either a concise and obtainable advent to the sector in addition to a close insurance at the exact examine issues of their strategies in a large spectrum of biometrics study starting from voice, face, fingerprint, iris, handwriting, human habit to multimodal biometrics. The contributions additionally current the pioneering efforts and state of the art effects, with designated specialize in sensible concerns relating approach improvement. This publication is a worthy reference for confirmed researchers and it additionally provides an outstanding creation for novices to appreciate the demanding situations.
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Extra resources for Advanced Topics In Biometrics
Comparison of background normalization methods for textindependent speaker veriﬁcation, in Proc. Eurospeech, pp. 2:963–966, 1997. Reynolds, D. A. Channel robust speaker veriﬁcation via feature mapping, in Proc. ICASSP, pp. 2:6–10, 2003. Reynolds, D. , Quatieri, T. , and Dunn, R. B. Speaker veriﬁcation using adapted Gaussian mixture modeling, Digital Signal Processing 10, 19–41, 2000. Reynolds, D. A. and Rose, R. Robust text-independent speaker identiﬁcation using Gaussian mixture speaker models, IEEE Trans.
Consonants are produced with an extreme narrowing or complete closing of the vocal tract, resulting in abrupt changes in the signal when the vocal tract closes (closure) and opens (release). The region between the closure and the release is also called the closure interval. Accordingly, there are usually two places in the signal that correspond to an underlying consonant. These times are marked [+consonantal]. The sonorant consonants show a continuation of quasi-periodic low frequency energy during the closure interval ([+sonorant]); the closure and release themselves are signaled by an abrupt decrease in high frequency energy ([−continuant]).
Such transformation matrices are estimated based on the ML criterion. In ML estimation, we assume the parameters are ﬁxed but unknown without any prior knowledge; instead, in MAP estimation, we assume the parameters belong to some prior PDFs, which have been proved as an eﬀective way in dealing with sparse training data. , 2008), has beneﬁted from the MAP learning. Let us assume that a speech utterance spoken by a speaker has been converted into a sequence of feature vectors, X = (x1 , . . , xT ), where xt is a D-dimensional vector.
Advanced Topics In Biometrics by Haizhou Li, Kar-Ann Toh, Liyuan Li