Appearance-based Face Recognition from robot camera images with Illumination and Distance Variations

This paper is concerned with the appearance-based face recognition from robot camera images with illumination and distance variations. The approaches used in this paper consist of eigenface, fisherface, and icaface, which are the most representative recognition techniques frequently used in conjunction with face recognition. These approaches are based on a popular unsupervised and supervised statistical technique that supports finding useful image representations, respectively. Thus we focus on the performance comparison from robot camera images with unwanted variations. The comprehensive experiments are completed for two face databases with illumination and distance variations. A comparative analysis demonstrates that ICA comes with improved classification rates when compared with other approaches such as eigenface and fisherface