The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

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

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

Towards regional fusion for high-resolution palmprint recognition

Abstract—The existing high resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy and focus on full-to-full/partial-to-full palmprint comparison. These algorithms would face problems when they are applied to forensic palmprint recognition where latent marks have much smaller area than full palmprints. Therefore, towards forensic scenarios, we propose a novel matching strategy based on regional fusion for high resolution palmprint recognition using regions segmented by major creases features. The matching strategy includes two stages: 1) region-to-region palmprint comparison; 2) regional fusion at score level. We first studied regional discriminability of a high resolution palmprint under the concept of three regions, i.e., interdigital, hypothenar and thenar, which is the most significant difference between palmprits and fingerprints. Then we implemented regional fusion based on logistic regression at score level using region-to-region comparison scores obtained by a commercial SDK, MegaMatcher 4.0. Significant improvement of recognition accuracy is achieved by regional fusion on a public high resolution palmprint database THUPALMLAB. The EER of logistic regression based regional fusion is 0.25%, while the EER of full-to-full palmprint comparison is 1%. Keywords-High resolution palmprints; regional fusion. I

R.P.: Towards Regional Fusion for High-Resolution Palmprint Recognition

Abstract-The existing high resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy and focus on full-to-full/partial-to-full palmprint comparison. These algorithms would face problems when they are applied to forensic palmprint recognition where latent marks have much smaller area than full palmprints. Therefore, towards forensic scenarios, we propose a novel matching strategy based on regional fusion for high resolution palmprint recognition using regions segmented by major creases features. The matching strategy includes two stages: 1) region-to-region palmprint comparison; 2) regional fusion at score level. We first studied regional discriminability of a high resolution palmprint under the concept of three regions, i.e., interdigital, hypothenar and thenar, which is the most significant difference between palmprits and fingerprints. Then we implemented regional fusion based on logistic regression at score level using region-to-region comparison scores obtained by a commercial SDK, MegaMatcher 4.0. Significant improvement of recognition accuracy is achieved by regional fusion on a public high resolution palmprint database THUPALMLAB. The EER of logistic regression based regional fusion is 0.25%, while the EER of full-to-full palmprint comparison is 1%