The extract region of interest in high-resolution palmprint using 2d image histogram entropy function

Abstract

The segmentation of high-resolution palmprint is has been challenged and the research in this filed is still limited because of variations in location and distortion of these images. To achieve superior recognition result, accurate segmentation of a region of interest is very crucial. Therefore, in this paper, a novel palmprint extraction method has been presented using a 2D image histogram entropy function and mathematical dilation. The proposed method has two phases. The first phase is the binarization image where the histogram of the image will be determined after applying a median filter to remove noise and then calculating the 2D image histogram entropy function. Finally, the maximum entropy that will be the adaptive threshold value to build a binary palmprint image will be selected. The second phase is to extract the ROI, apply a dilation method on the binary image, then dividing the dilate image into four regions and finding four reference points depending on the white percentage and finally the ROI will be extracted. The publically available high-resolution palmprint THUPALMLAB has been used for testing. The result indicates a high percentage of accuracy up to 93%. The findings strongly indicate that the proposed method was able to extract the palm's ROI more consistently. These ROIs will be used in the recognition system instead of whole palmprints and hence assists in improving the performance of a traditional palmprint system. High-resolution palmprint images are highly used in the forensic application.