Biometrics and Kansei Engineering by Khalid Saeed (auth.), Khalid Saeed, Tomomasa Nagashima

By Khalid Saeed (auth.), Khalid Saeed, Tomomasa Nagashima (eds.)

Biometrics and Kansei Engineering is the 1st publication to collect the foundations and purposes of every self-discipline. the way forward for biometrics is wanting new applied sciences which can rely on people’s feelings and the prediction in their purpose to take an motion. Behavioral biometrics reports the best way humans stroll, speak, and convey their feelings, and Kansei Engineering specializes in interactions among clients, products/services and product psychology. they're turning into rather complementary.

This e-book additionally introduces biometric functions in our surroundings, which additional illustrates the shut courting among Biometrics and Kansei Engineering. Examples and case experiences are supplied all through this publication.

Biometrics and Kansei Engineering is designed as a reference e-book for execs operating in those similar fields. Advanced-level scholars and researchers learning computing device technology and engineering will locate this booklet worthwhile as a reference or secondary textual content booklet to boot.

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A) Euclidean distance is unable to differentiate between individuals. (b) The learned manifold of classifier is unable to characterize unseen images of the same individual face difficult to extract the consistent intrinsic information of the face objects from their respective images. As illustrated above, the entire face manifold is highly nonconvex, and so is the face manifold of any individual under various circumstances. , the image space) to a low-dimensional subspace. Because of this fact, they are unable to preserve the nonconvex variations of face manifolds necessary to differentiate among individuals.

This variability makes it 26 P. Kocjan and K. Saeed Fig. 3 Similarity of frontal faces between son and father (a) [81, 82], twins (b) [81] Fig. 4 Challenges in face recognition from subspace viewpoints. (a) Euclidean distance is unable to differentiate between individuals. (b) The learned manifold of classifier is unable to characterize unseen images of the same individual face difficult to extract the consistent intrinsic information of the face objects from their respective images. As illustrated above, the entire face manifold is highly nonconvex, and so is the face manifold of any individual under various circumstances.

Tabor Jagiellonian University, Krako´w, Poland K.

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