By Haizhou Li, Kar-Ann Toh, Liyuan Li
Biometrics is the learn of tools for uniquely spotting people in line with a number of intrinsic actual or behavioral qualities. After a long time of analysis actions, biometrics, as a well-known clinical self-discipline, has complicated significantly either in sensible know-how and theoretical discovery to satisfy the expanding want of biometric deployments. during this e-book, the editors offer either a concise and available advent to the sphere in addition to a close assurance at the precise study issues of their strategies 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 cutting-edge effects, with designated concentrate on functional concerns touching on approach improvement. This e-book is a useful reference for demonstrated researchers and it additionally supplies an outstanding creation for newcomers to appreciate the demanding situations.
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Extra resources for Advanced Topics In Biometrics
There has been an eﬀort in recent years to construct speech recognition systems based on distinctive features and acoustic correlates, as described in this chapter. , landmark detection. Next, distinctive features from the speech signal will be found by searching for acoustic cues around landmarks. The extracted distinctive features will then be consolidated into segments. These segments will not always be directly identiﬁable as canonical phonemes; further processing to deduce any modiﬁcations that may have occurred, and to infer the underlying phonemes, will be required next.
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.
A UBM of 512 GMM components, SVM background, and NAP projection matrix was trained on SRE04 data. 5in Advanced Topics in Biometrics b1057-ch01 B. Ma and H. Li 24 where si is the score from the ith classiﬁers and N = 7 is the total number of classiﬁers. 0 and w0 is given by the threshold, which yields minimum DCF. The core test of SRE08 was designed to have a short2 training condition, which involves either telephone or interview speech, and a short3 test segment condition, which involves telephone, microphone, or interview speech.
Advanced Topics In Biometrics by Haizhou Li, Kar-Ann Toh, Liyuan Li