(Two-Dimensional)2 whitening reconstruction for newborn recognition

Recently, various feature extraction techniques and its variations have been proposed for computer vision. However, most of these techniques are sensitive to the images acquired in uncontrolled environment. The illumination, expression and occlusion for face images result in random error entries in the 2days matrix representing the face. Techniques such as Principal Component Analysis (PCA) do not handle these entries explicitly. This paper proposes a (Two-dimensional)² whitening reconstruction (T²WR) pre-processing step to be coupled with the PCA algorithm. This combined method would process illumination & expression variations better than standalone PCA. This technique has been compared with state-of-the-art Two-dimensional whitening reconstruction (TWR) pre-processing method. The final results clearly indicate the reason for better performance of T²WR over TWR. The histograms plotted for both these algorithms show that T²WR makes for a smoother frequency distribution than TWR. The proposed method indicated increased recognition rate and accuracy with increasing number of training images; up to 93.82 % for 2 images, 94.76 % for 4 images and 97.42 % for 6 training images.