Long Range Cross-Spectral Face Recognition: Matching SWIR Against Visible Light Images

• Published in: IEEE transactions on information forensics and security Vol. 7; no. 6; pp. 1717 - 1726
• Main Authors: Nicolo, F., Schmid, N. A.
• Format: Journal Article
• Language: English
• Published: IEEE 01.12.2012
• Subjects: Cameras; Encoding; Face recognition; Feature extraction; Gabor wavelets; generalized local binary pattern; heterogeneous face matching; I-divergence; Infrared imaging; simplified Weber local descriptor; SWIR
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2012.2213813

Short wave infrared (SWIR) is an emerging imaging modality in surveillance applications. It is able to capture clear long range images of a subject in harsh atmospheric conditions and at night time. However, matching SWIR images against a gallery of color images is a very challenging task. The photometric properties of images in these two spectral bands are highly distinct. This work presents a novel cross-spectral face recognition scheme that encodes images filtered with a bank of Gabor filters followed by three local operators: Simplified Weber Local Descriptor, Local Binary Pattern, and Generalized Local Binary Pattern. Both magnitude and phase of filtered images are encoded. Matching encoded face images is performed by using a symmetric I-divergence. We quantify the verification and identification performance of the cross-spectral matcher on two multispectral face datasets. In the first dataset (PRE-TINDERS), both SWIR and visible gallery images are captured at a close distance (about 2 meters). In the second dataset (TINDERS), the probe SWIR images are collected at longer ranges (50 and 106 meters). The results on PRE-TINDERS dataset form a baseline for matching long range data. We also demonstrate the capability of the proposed approach by comparing its performance with the performance of Faceit G8, a commercial face recognition engine distributed by L1. The results show that the designed method outperforms Faceit G8 in terms of verification and identification rates on both datasets.