Iris Recognition at Airports and Border-Crossings
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levels, including sociolinguistic factors (e.g., level of education, linguistic context, and dialectal differences) and morphological issues (e.g., vocal tract length and shape or the dynamic configuration of the articulatory organs). These multiple influences will be simultaneously present in each speech act and some or all of them will contain specificities of the speaker. Hence, it is needed to clarify and clearly distinguish the different levels and sources of speaker information that should be extracted to model speaker individualities. ▶ Speaker Features
ID Photograph ▶ Photography for Face Image Data
Identification Biometric identification is a process that ranks the biometric references in the enrolment database in order of decreasing similarity against a recognition biometric sample and then makes a decision, based on the similarity scores, about the identity w.r.t. to the references. ▶ Verification/Identification/Authentication/Recogni-
tion: The Terminology
Identity Level in the Speech Signal Speech production is an extremely complex process, whose result depends on many variables at different #
2009 Springer Science+Business Media, LLC
Identity Theft Reduction ▶ Fraud Reduction, Overview
Illumination Ambient light sources may affect the appearance of a biometric image (such as face, fingerprint, or iris). The intensity and direction of these light sources can impact the performance of image-based biometric recognition algorithms. ▶ Biometrics, Overview ▶ Face Recognition, Near-Infrared ▶ Face Tracking ▶ Illumination Compensation ▶ Photography for Face Image Data
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Illumination Compensation
Illumination Compensation X UDONG X IE 1, K IN -M AN L AM 2, Q IONGHAI DAI 1 1 Automation Department, Tsinghua University, Beijing, China 2 Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Synonyms Lighting compensation; Illumination normalization
Definition Due to difficulty in controlling the lighting conditions in practical applications, variable illumination is one of the most challenging tasks in face recognition. Prior to face recognition, illumination compensation has to be performed, whereby the uneven illumination of human faces is compensated and face images in normal lighting conditions are reconstructed. The reconstructed face images are then used for classification. An illumination compensation scheme includes the following modules: lighting category evaluation, shape normalization, and lighting compensation.
Introduction Human face recognition, one of the most successful applications of image analysis and understanding, has received significant attention in the last decade. However, due to difficulty in controlling the lighting conditions in practical applications, variable illumination is one of the most daunting challenges in face recognition. As stated by Adini et al. [1], ‘‘The variations between the
images of the same face due to illumination and viewing direction are almost always larger than image variations due to chang
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