Face recognition in the SWIR band when using single sensor multi-wavelength imaging systems

• Published in: Image and vision computing 2015-01, Vol.33, p.26-43
• Main Authors: Narang, N., Bourlai, T.
• Format: Article
• Language: English
• Subjects: Automated; Cameras; Classification; Face recognition; Imaging; Multi-spectral imaging; Pre-processing; Sensors; SWIR band; Wavelengths; Weighted score level fusion scheme
• ISSN: 0262-8856; 1872-8138
• DOI: 10.1016/j.imavis.2014.10.005

In this paper, we study the problem of Face Recognition (FR) when using Single Sensor Multi-Wavelength (SSMW) imaging systems that operate in the Short-Wave Infrared (SWIR) band. The contributions of our work are four fold: First, a SWIR database is collected when using our developed SSMW system under the following scenarios, i.e. Multi-Wavelength (MW) multi-pose images were captured when the camera was focused at either 1150, 1350 or 1550nm. Second, an automated quality-based score level fusion scheme is proposed for the classification of input MW images. Third, a weighted quality-based score level fusion scheme is proposed for the automated classification of full frontal (FF) vs. nonfrontal (NFF) face images. Fourth, a set of experiments is performed indicating that our proposed algorithms, for the classification of (i) multiwavelength images and (ii) FF vs. NFF face images, are beneficial when designing different steps of multi-spectral face recognition (FR) systems, including face detection, eye detection and face recognition. We also determined that when our SWIR-based system is focused at 1350nm, the identification performance increases compared to focusing the camera at any of the other SWIR wavelengths available. This outcome is particularly important for unconstrained FR scenarios, where imaging at 1550nm, at long distances and when operating at night time environments, is preferable over different SWIR wavelengths. •Developed SSMW system in SWIR band can acquire a series of images in short duration.•Proposed an automated quality score fusion scheme for classification of MW images.•Proposed an automated method for classification of frontal vs non frontal face images.•Proposed algorithms are beneficial when designing face recognition systems.