Interpolation of low resolution images for improved accuracy in human face recognition

This paper presents the effect of interpolation schemes, namely, nearest-neighbor, bilinear, and bicubic, when applied to low-resolution images as a preprocessing step for improving the recognition rate in human face recognition system. Three feature extraction methods are used, namely, Local Binary Pattern, Discrete Wavelet Transform, and Block-Based Discrete Cosine Transform, with and without interpolation for comparison purpose. The experiments are conducted on the ORL database. Bicubic and bilinear improve the recognition rate of low resolution images considerably.