By Haizhou Li, Kar-Ann Toh, Liyuan Li

Biometrics is the research of equipment for uniquely spotting people according to a number of intrinsic actual or behavioral features. After many years of study actions, biometrics, as a well-known medical self-discipline, has complicated significantly either in functional know-how and theoretical discovery to fulfill the expanding want of biometric deployments. during this booklet, the editors supply either a concise and obtainable creation to the sector in addition to an in depth insurance at the distinct study issues of their options in a large spectrum of biometrics examine 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 targeted specialise in functional matters referring to process improvement. This booklet is a worthy reference for proven researchers and it additionally offers a great creation for newcomers to appreciate the demanding situations.

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Extra resources for Advanced Topics In Biometrics

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The SRE04 data was used as the background dataset; the telephone and microphone data from SRE04, SRE05, and SRE06, and the interview data of Mixer 5 were applied for NAP training; and the 1conv4w telephone data of SRE05 were used for T -norm. For the NAP training, the same scheme of individual channel space training and subspace combination as that for JFA was adopted. , 2006b). Given a speaker’s utterance, a GMM is estimated by using MAP-adapted means of the UBM of 512 Gaussian mixture components.

Given two speech utterances, a and b, we can derive two supervectors, ma and b m , in the same way as in Sec. 2. 31) where P is a projection (P2 = P), v is the direction being removed from the SVM expansion space, b(·) is the SVM expansion, and v 2 = 1. 32) ij where the {mi } and {mj } are typically a background dataset. The elements of the symmetric matrix W can be designed as positive for the pairs of training utterances that are to be pulled together, negative for pairs that are to be pushed apart, and zero for pairs that are of no concern.

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. Speech and Audio Processing 3, 1, 72–83, 1995. , Campbell, W. , and Boardman, I. Advances in channel compensation for SVM speaker recognition, in Proc.

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