How Low Can You Go? Low Resolution Face Recognition Study Using Kernel Correlation Feature Analysis on the FRGCv2 dataset
Ramzi Abiantun, Marios Savvides, B. V. K. Vijaya Kumar · 2006
In this paper we investigate the effect of image resolution of the Face Recognition Grand Challenge (FRGC) dataset on the Kernel Class-dependence Feature Analysis (KCFA) method. Good performance on low-resolution image data is important for any face recognition system using low-resolution imagery, such as in surveillance footage. We show that KCFA works reliably even at very low resolutions on the FRGC dataset Experiment 4 using the one-to-one matching protocol (greater than 70% verification rate (VR) at 0.1% false accept rate (FAR)). We observe reasonable performance at resolution as low as 16×16. However performance of KCFA degrades significantly below this resolution, but still outperforms the PCA baseline algorithm with 12% VR at 0.10% FAR.