By Haizhou Li, Kar-Ann Toh, Liyuan Li
Biometrics is the learn of tools for uniquely spotting people according to a number of intrinsic actual or behavioral features. After a long time of analysis actions, biometrics, as a famous medical self-discipline, has complicated significantly either in sensible know-how and theoretical discovery to fulfill the expanding want of biometric deployments. during this ebook, the editors offer either a concise and available creation to the sphere in addition to a close assurance at the precise study issues of their options in a large spectrum of biometrics learn starting from voice, face, fingerprint, iris, handwriting, human habit to multimodal biometrics. The contributions additionally current the pioneering efforts and cutting-edge effects, with specific specialize in sensible matters pertaining to approach improvement. This e-book is a beneficial reference for demonstrated researchers and it additionally supplies a very good creation for newcomers to appreciate the demanding situations.
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Extra info for Advanced Topics In Biometrics
These sounds may be described as vowels with oﬀ-glides. A separate oﬀ-glide landmark is thus assigned to a time near the end of the sound. This event is marked [+glide], and is assigned articulator-bound features associated with the oﬀ-glide. Glides are produced with an intermediate narrowing of the vocal tract, so that there are no abrupt discontinuities from an adjacent vowel. Glide landmarks are chosen at times where the signal amplitude becomes a minimum (Sun, 1996). The [+glide] feature is marked for those times.
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.
5. DET curves of individual classifiers and the fused system on the SRE08 telephonetelephone condition. 5in Advanced Topics in Biometrics b1057-ch01 B. Ma and H. 30% Improvement — advancement of many new techniques around GMMs and SVMs. The recent progress features the use of high-dimensional feature vectors, which are also called supervectors (Kinnunen and Li, 2010). It should be noted that the compensation of the session variability plays a critical role in system design. With the invention of JFA and NAP, the speaker recognition performance in mismatched acoustic environments has been greatly enhanced.