Small Sample Biometric Recognition Based on Palmprint and Face Fusion

Contactless biometrics provide high comfort and hygiene in person recognition. Because of this, such systems are better accepted by the general public. This paper proposes an adaptive, contactless, biometric system which combines two modalities: palmprint and face. The processing chain has been designed to overcome embedded system constraints and small sample set problem: after a palmprint is extracted from a hand image, Gabor filters are applied to both the palmprint and face in order to extract parameters, which are then used for classification. Fusion possibilities are also discussed and tested using a multimodal database of 130 people designed by the authors. High recognition performance has been obtained by respecting embedded system context, with palmprint only and with fusion of palmprint and face: recognition rates of respectively 96.39% and 98.85% are achieved using only 2 samples per modality. Therefore this preliminary study shows the feasibility of a robust and efficient multimodal hardware biometric system.