The paper focuses on personally identifiable information (PII) and interoperability. The emphasis is on quantitative performance analysis and validation for uncontrolled operational settings and image quality, and variable demographics and gallery composition. Biometric face authentication involves three distinct operational stages, those of face space learning (training), gallery enrollment, and testing (querying). The authentication tasks considered here are face identification and verification. Performance evaluation involves k-fold cross-validation using both PCA and LDA for face space representation. Our basic findings indicate that (a) training to learn the face space is less important than the quality of images during enrollment and testing;(b) exclusion of first eigenvectors in defining the face space improves performance particularly for the PCA face space and lesser quality data; (c) the size of the subject gallery affects performance; and (d) it does not make much difference if the face space is derived from biometric data coming from the same dataset source as that used for enrollment and testing. Possible solutions to enhance overall performance and cope with adversarial (impostor) behavior during mass screening include non-inductive learning settings, e.g., transduction and transfer learning, using both labeled and unlabeled examples. © 2012 IEEE.

Biometric interoperability across training, enrollment, and testing for face authentication / H., El Khiyari; DE MARSICO, Maria; A. f., Abate; H., Wechsler. - ELETTRONICO. - (2012), pp. 1-8. (Intervento presentato al convegno 2012 3rd IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, BioMS 2012 tenutosi a Salerno nel 14 September 2012 through 14 September 2012) [10.1109/bioms.2012.6345777].

Biometric interoperability across training, enrollment, and testing for face authentication

DE MARSICO, Maria;
2012

Abstract

The paper focuses on personally identifiable information (PII) and interoperability. The emphasis is on quantitative performance analysis and validation for uncontrolled operational settings and image quality, and variable demographics and gallery composition. Biometric face authentication involves three distinct operational stages, those of face space learning (training), gallery enrollment, and testing (querying). The authentication tasks considered here are face identification and verification. Performance evaluation involves k-fold cross-validation using both PCA and LDA for face space representation. Our basic findings indicate that (a) training to learn the face space is less important than the quality of images during enrollment and testing;(b) exclusion of first eigenvectors in defining the face space improves performance particularly for the PCA face space and lesser quality data; (c) the size of the subject gallery affects performance; and (d) it does not make much difference if the face space is derived from biometric data coming from the same dataset source as that used for enrollment and testing. Possible solutions to enhance overall performance and cope with adversarial (impostor) behavior during mass screening include non-inductive learning settings, e.g., transduction and transfer learning, using both labeled and unlabeled examples. © 2012 IEEE.
2012
2012 3rd IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, BioMS 2012
authentication; face identification; face space; face verification; interoperability; lda; pca; validation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Biometric interoperability across training, enrollment, and testing for face authentication / H., El Khiyari; DE MARSICO, Maria; A. f., Abate; H., Wechsler. - ELETTRONICO. - (2012), pp. 1-8. (Intervento presentato al convegno 2012 3rd IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, BioMS 2012 tenutosi a Salerno nel 14 September 2012 through 14 September 2012) [10.1109/bioms.2012.6345777].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/482823
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