Soft Biometric Traits for Personal Recognition Systems

By Anil K. Jain, Sarat Chandra Dass, Karthik Nandakumar. International Conference on Biometric Authentication, 2004

473 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/67546c8456d7f2a1c381d2aa5a1eb0b83358f631

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In Computer Vision and Pattern Recognition

In Information Systems

In Signal Processing

Citations in the corpus

Pattern Recognition (2018, unknown): “Jain et al. [3] proposed a hybrid system that combined ﬁngerprint identiﬁcation with soft biometric attributes to improve the overall matching accuracy.”
Biometrics: a grand challenge (2004, background): “Consequently, a two-pronged approach is necessary to offer an effective identification in screening systems: (i) exploring effective methods of spatio-temporally utilizing weak biometric cues (also called soft biometrics [21]) such as height, gait, hair color, coarse facial features, etc.”
Face Recognition Vendor Test (FRVT) - Performance of Automated Age Estimation Algorithms (2014, background): “Age estimates are a soft biometric [16] and its characterization has become a growing area of study given its potential use in various applications.”
Person Re-identification by Attributes (2012, methodology): “In a surveillance context, the semantic nature of the attribute representation has been used in initial research on attribute-based person search, that aims to retrieve instances of all people matching a verbal attribute description from a camera network [23].”
KinectFaceDB: A Kinect Database for Face Recognition (2014, background): “Such facial details can be used as the soft biometric characteristics [74], [75], which can also provide useful information for the identity discrimination.”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “The term demographics, in addition to referring to the quantifiable statistics of a given population, refers to attributes such as age, gender, ethnicity and race that are widely used in common population statistics.”
Matching Forensic Sketches to Mug Shot Photos (2011, methodology): “In order to improve the accuracy of matching forensic sketches, we utilize ancillary or demographic information provided by the witness, tobeused asasoft biometric [ 34 ].For example,suppose the witness reports that the race of the culprit is Caucasian; then we can eliminate all non-Caucasian member”
Periocular Biometrics in the Visible Spectrum (2011, background): “3) The periocular region can offer information about eye shape that may be useful as a soft biometric [12], [13].”
MORPH: a longitudinal image database of normal adult age-progression (2006, background): “soft biometric models [10], which can account for the effects of weight on appearance or the role of the ethnicity in age-progression.”
Age Synthesis and Estimation via Faces: A Survey (2010, background): “Age estimation is a type of soft biometrics [18] that provides ancillary information of the users’ identity information.”
Behavioural biometrics: a survey and classification (2008, background): “The majority of research is aimed at studying well-established physical biometrics such as fingerprint (Cappelli et al,, 2006) or iris scans (Jain et al., 2004d).”