A SVM-Based Model for the evaluation of biometric sample quality

Mohamad El-Abed (1) , Romain Giot (1) , Baptiste Hemery (1) , Christophe Charrier (1) , Christophe Rosenberger (1)

1 Equipe SAFE - Laboratoire GREYC - UMR6072

One of the main factors affecting the performance of biometric systems is the quality of the acquired samples. Poorquality samples increase the enrollment failure, and decrease the system performance. Therefore, it is important for a biometric system to estimate the quality of the acquired biometric samples. Toward this goal, we present in this paper a multi-class SVMbased method to predict sample quality. The proposed method uses two types of information: the first one is based on the image quality and the second is a pattern-based quality using the SIFT keypoints extracted from the image. For the experiments, we use four large and significant face databases to show the efficiency of the proposed method in predicting the system performance illustrated by the Equal Error Rate (EER).

Mots clés: Biometrics; quality assessment; performance; Support Vector Machine (SVM); Scale-Invariant Feature Transform (SIFT)

Domaines: Cryptographie et sécurité [cs.CR]

Mohamad El-Abed, Romain Giot, Baptiste Hemery, Christophe Charrier, Christophe Rosenberger. A SVM-Based Model for the evaluation of biometric sample quality. IEEE Symposium Series in Computational Intelligence - CIBIM workshop, 2011, Paris, France. pp.115-122, ⟨10.1109/CIBIM.2011.5949212⟩. ⟨hal-00992462⟩