By Kieron Messer, Josef Kittler, James Short, G. Heusch, Fabien Cardinaux (auth.), David Zhang, Anil K. Jain (eds.)
This publication constitutes the refereed complaints of the overseas convention on Biometrics, ICB 2006, held in Hong Kong, China in January 2006.
The 104 revised complete papers awarded have been conscientiously reviewed and chosen from 192 submissions. Biometric standards coated by way of the papers are assigned to stand, fingerprint, iris, speech and signature, biometric fusion and function review, gait, keystrokes, and others. additionally the result of the Face Authentication festival (FAC 2006) also are introduced during this volume.
Read Online or Download Advances in Biometrics: International Conference, ICB 2006, Hong Kong, China, January 5-7, 2006. Proceedings PDF
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Extra info for Advances in Biometrics: International Conference, ICB 2006, Hong Kong, China, January 5-7, 2006. Proceedings
These algorithms can be used in developing a facial image quality assessment system (FIQAS) that works as a preprocessing module for any face recognition method. The idea of FIQAS is to assess the quality of facial images and either reject or accept them for the recognition step. We focus on assessing the eﬀect of blurring and facial expressions on facial images. In order to develop the algorithms for assessing the quality of facial images, the challenge is to measure the level or the intensity1 of the factors that aﬀect the quality of the facial images.
25 Displacement Error Threshold (a) (b) Fig. 1. (a) A picture of face capture system. (b) The automatic eye center detection results for LED faces and ambient faces. LED faces (faces under only LED illumination). Two capture sessions have been conducted with a time interval of several weeks. For each session, 4 diﬀerent illumination conﬁgurations are used with light sources directed individually from left, bottom, right and top. 6 recordings were acquired for each illumination conﬁguration. LED illumination is provided by a LED lamp with peak out wavelength at 850nm.
For blurring eﬀect, measuring the level of blurriness is possible. On the other hand, measuring the intensity of face expression is diﬃcult because of the absence of the reference neutral face image. Considering the issues discussed above, we take two diﬀerent strategies to assess the quality of facial images: one strategy for blurring eﬀect and another strategy for facial expressions. For blurring eﬀect, we develop a function for predicting the performance rate of the Eigenface recognition method on images 1 In this paper, the word intensity is synonymous with the word level.