Performance of face recognition with pre-processing techniques on Robust Regression method

The Robust Regression method has been used successfully in face recognition problems. Based on empirical experiments on some standard face image databases, the method shows very high accuracy. The method used the histogram equalization technique to normalize illumination such that the effect of illumination factors is reduced substantially on the image. In this research, some contrast adjustment techniques are used in the pre-processing stage to determine how far those techniques affect the face recognition performance. There are three contrast adjustment techniques used, i.e. Histogram Equalization (Histeq function), Contrast-limited Adaptive Histogram Equalization / CLAHE (Adapthisteq function) and Imadjust function. In addition, it is also used the no-pre-processing technique (not using pre-processing techniques). The experiments were performed on three standard face image databases, i.e. CMU-PIE Face Database, Extended Yale Face Database B, and AR Face Database. The experimental results show that the use of Adapthisteq function in the pre-processing stage of the Robust Regression method produces the highest average accuracy of 97.69%. This result is better than the accuracy of Histeq, Imadjust, or no-pre-processing technique, which are 94.53%, 90.59%, and 93.43% respectively.