Journal ArticleDOI
Label propagation approach for predicting missing biographic labels in face-based biometric records
Thomas Swearingen,Arun Ross +1 more
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TLDR
This work proposes the use of a graph structure to model the relationship between the biometric records in a database and shows the benefits of such a graph in deducing biographic labels of incomplete records, i.e. records that may have missing biographic information.Abstract:
A biometric system uses the physical or behavioural attributes of a person, such as face, fingerprint, iris or voice, to recognise an individual. Many operational biometric systems store the biographic information of an individual, viz., name, gender, age and ethnicity, besides the biometric data itself. Thus, the biometric record pertaining to an individual consists of both biometric data and biographic data. We propose the use of a graph structure to model the relationship between the biometric records in a database. We show the benefits of such a graph in deducing biographic labels of incomplete records, i.e. records that may have missing biographic information. In particular, we use a label propagation scheme to deduce missing values for both binary-valued biographic attributes (e.g. gender) as well as multi-valued biographic attributes (e.g. age group). Experimental results using face-based biometric records consisting of name, age, gender and ethnicity convey the pros and cons of the proposed method.read more
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A comprehensive overview of biometric fusion
TL;DR: A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is presented in this paper, where the authors provide a comprehensive overview of the role of information fusion in biometrics.
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.
Karl Ricanek,Tamirat Tesafaye +1 more
TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
Posted Content
A Comprehensive Overview of Biometric Fusion
TL;DR: The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics with specific focus on three questions: what to fusion, when to fuse, and how to fuse.
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FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers
TL;DR: Wang et al. as discussed by the authors proposed stacking a diverse set of SAN models to compensate each other's weaknesses, thereby forming a robust model with improved generalization capability, which enables SANs to generalize to multiple gender classifiers.
Proceedings ArticleDOI
Real-Time Eye-Gaze Based Interaction for Human Intention Prediction and Emotion Analysis
TL;DR: The methodology analyzes human emotion and cognition status from the aspect of eye-gaze behavior and head motion, understands the cognitive information that human eyes can express, and effectively improves the efficiency of human-computer interaction in different circumstances.
References
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Race classification from face images using local descriptors
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Counting on the Census? Race, Group Identity, and the Evasion of Politics
Proceedings ArticleDOI
Human face identification via comparative soft biometrics
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Proceedings ArticleDOI
Facial ethnicity classification based on boosted local texture and shape descriptions
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Proceedings ArticleDOI
Soft biometric retrieval to describe and identify surveillance images
TL;DR: A baseline solution to the problem of identifying individuals solely from human descriptions, by automatically retrieving soft biometric labels from images, is presented, which reports the increased retrieval accuracy of binary labels, the generalising capability of continuous measurements and the overall performance improvement of comparative annotations over categorical annotations.