A new face database simultaneously acquired in visible, near infrared and thermal spectrum
This work addresses face recognition challenges for biometric security by providing a novel dataset, but it is incremental as it builds on existing multi-spectral approaches.
The authors tackled the problem of face identification by creating a new multi-spectral database with visible, near-infrared, and thermal images under varied illumination conditions, and found that combining all three spectral bands significantly improved identification rates, achieving over 98% in most scenarios with a trained rule.
In this paper we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7.380 pictures. Experimental results are obtained through single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three spectral bands studied contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine scenarios studied we obtained identification rates higher or equal to 98%, when using a trained combination rule, and two cases of nine when using a fixed rule.