Impact of image preprocessing on face recognition: A comparative analysis

Abstract

Quality of image plays a vital role in increasing face recognition rate. A good quality image gives better recognition rate than noisy images. Recent research on face recognition reveals that image capturing devices such as video cameras, also impacts the recognition rate as sometimes they capture low quality and/or noisy images due to their poor quality. In such case the images captured will have low resolution, noisy, contrast variation and/or varying brightness throughout the image. It is more difficult to extract features from such noisy images which in-turn reduces face recognition rate. To overcome problems occurred due to low quality image, pre-processing is done before extracting features from the image. In this paper we will analyze the effect of pre-processing prior to feature extraction process with respect to the face recognition rate. This also gives a qualitative description of various pre-processing techniques and feature extraction schemes that were used for our analysis. The results were analyzed with the help of bar graphs. In our research, we found that the combined method of feature extraction (Spatial and Frequency) shows superior performance than individual feature extraction schemes. It was also found that, this combined method gives good recognition results even without pre-processing of the image.