A Review: Personal Identification Based on Palm Vein Infrared Pattern

Palm vein recognition is the latest biometrics technique used and researches currently. This method achieved high performance in identification due to the complexity of vein pattern on the palm. This studies proposed a review of overall process of vein recognition and vein recognition techniques. In particular, this studies is systematically described in three parts which is vein image acquisition and preprocessing, feature extraction and decision matching. According to the available work, various approaches for different kind of features extractions, palm vein segmentation and overall process will be discussed in this paper

Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins

A review: personal identification based on palm vein infrared pattern

Palm vein recognition is the latest biometrics technique used and researches currently. This method achieved high performance in identification due to the complexity of vein pattern on the palm. This studies proposed a review of overall process of vein recognition and vein recognition techniques. In particular, this studies is systematically described in three parts which is vein image acquisition and preprocessing, feature extraction and decision matching. According to the available work, various approaches for different kind of features extractions, palm vein segmentation and overall process will be discussed in this paper

Three-Dimensional Convolutional Approaches for the Verification of Deepfake Videos: The Effect of Image Depth Size on Authentication Performance

Deep learning has proven to be particularly effective in tasks such as data analysis, computer vision, and human control. However, as this method has become more advanced, it has also led to the creation of DeepFake video sequences and images in which alterations can be made without immediately appealing to the viewer. These technological advancements have introduced new security threats, including in the field of education. For example, in online exams and tests conducted through video conferencing, individuals may use Deepfake technology to impersonate another person, potentially allowing them to cheat by having someone else take the exam in their place. Several detection approaches have been proposed to address these issues, including systems that use both spatial and temporal features. However, existing approaches have limitations regarding detection accuracy and overall effectiveness. The paper proposes a technique for detecting Deepfakes that combines temporal analysis with convolutional neural networks. The study explores various 3-D Convolutional Neural Networks-based (CNN-based) model approaches and different sequence lengths of facial photos. The results indicate that using a 3-D CNN model with 16 sequential face images as input can detect Deepfakes with up to 97.3 percent accuracy on the FaceForensic dataset. Detecting Deepfakes is crucial as they pose a threat to the authenticity of visual media. The proposed technique offers a promising solution to this issue

Intelligent hand vein image exposure system to aid peripheral intravenous access

Difficulty in achieving peripheral intravenous (N) access in some patients is a clinical problem. These difficulties may lead to some negative impacts such as fainting, hematoma and pain associated with multiples punctures. As a result, ultrasound and infrared imaging devices have been used to aid IV access. Although these devices have shown to be able to aid IV access, infrared system has not been able to produce satisfactorily clear vein patterns and using ultrasound device is time consuming. Therefore, this research focuses on developing a hand vein exposure system with enhanced image of hand vein patterns to aid IV access. It consists of three major sub-systems namely, a hand vein image-acquisition system, image processing component and hand vein image-projection system. The image acquisition system consists of forty eight near-infrared light emitting diode with wavelength of 0.89um. The image processing system involves six stages. In the first stage, a noisy hand vein image is filtered using a feed-forward neural network (FFNN) based on standard median filter. In the second stage, a newly proposed technique based on finger-webs and finger-tips characteristics is applied to obtain a larger region of interest (ROl). In the third stage, the ROl images are enhanced using a combination of fuzzy histogram hyperbolization and contrast limited adaptive histogram equalization. Then, in the fourth stage, vein patterns are segmented using local adaptive threshold. In the fifth stage, a noisy binary vein patterns are enhanced using a combination of FINN pixel correction, binary median filter and massive noise removal. In the last stage, an enhanced vein patterns are registered into the original hand vein layout. Finally, the last sub-system projects the registered vein patterns onto a patient's hand

Grayscale and Binary Image Enhancement of Hand Vein Images to Aid Peripheral Intravenous Access

Difficulty in achieving a peripheral intravenous (IV) access in pediatric and some adult’s patient is a clinical problem. The use of near-infrared imaging device to aid visualization of an IV access usually suffers from low contrast and noise due to nonillumination and thickness of hand skin. This further complicates subsequent processing such as image segmentation. In this work, two methods are proposed in two different stages; grayscale enhancement and binary enhancement for correction of low contrast and noisy images. For grayscale enhancement, a combination of histogrambased and fuzzy-based contrast enhancement algorithms are applied on hand vein images. For binary enhancement, a combination of three techniques; Artificial Neural Network pixel corrector, Binary Median Filter and Massive Noise Removal, are applied on the binary hand vein images. Comparative analysis on test images using different contrast enhancement methods has shown superior results from the proposed method in comparison to its counterparts

New Technique for Larger ROI Extraction of Hand Vein Images

Region of Interest (ROI) extraction is a crucial step in automatic hand vein biometric and biomedical systems. The aim of ROI extraction is to decide which part of the image is suitable for hand vein feature extraction. The majority vein patterns sometimes can be determined at different locations; left, right and centre of the back of hand. The existing methods have not been able to extract more vein patterns at the right and left borders of the ROI. This paper proposes a hand vein ROI extraction method which is robust at avoiding loss of vein patterns information along the right and left borders of the ROI. First, we determine the threshold value, which will be used to segment the hand region. Second, the hand image is traced using boundary tracing. Third, the Euclidean distance is measured between reference point and hand boundary. Fourth, the distribution diagrams are constructed for the feature points selection. Finally, four coordinates are determined prior to ROI extraction. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods

Grayscale and binary enhancement of dorsal hand vein images

Difficulty in achieving a peripheral intravenous (IV) access in pediatric and some adult patients is a clinical problem. These difficulties may lead to some negative impacts such as fainting, hematoma and pain associated with multiples punctures. The use of near-infrared imaging device to aid visualization of an IV access usually suffers from low contrast and noise due to non-illumination and thickness of hand skin. This further complicates subsequent processing such as image segmentation. In this work, two methods are proposed in two different stages; grayscale enhancement and binary enhancement for correction of low contrast and noisy images. For grayscale enhancement, a combination of histogram-based and fuzzy-based contrast enhancement algorithms are applied on hand vein images. For binary enhancement, a combination of three techniques; Artificial Neural Network pixel corrector, Binary Median Filter and Massive Noise Removal, are applied on the binary hand vein images. Comparative analysis on test images using the proposed different contrast enhancement methods has shown superior results in comparison to its counterparts

Palm vein recognition using scale invariant feature transform with RANSAC mismatching removal

Palm vein recognition has getting more attention and popular among all other biometrics methods. In order to apply this type of recognition system to society, obtain an accurate reading robustly and effectively become the most pop-ular research topic in this field. However, there are still an unsolved issues on accurate palm vein recognition although there are several research done. In this paper, impact of Random Sample Consensus (RANSAC) point mismatching re-moval and different wavelength spectrum to the recognition rate will be dis-cussed. CASIA Multi Spectral Palm Print Image database is used for this re-search. Scale Invariant Feature Transform (SIFT) and RANSAC mismatching removal will be adopted for vein extraction and point feature matching with Eu-clidean Distance. The results shows that SIFT algorithm with RANSAC mis-matching point removal achieved better recognition rate than without mismatch-ing point removal technique used. It can be proved that RANSAC mismatching point removal are able to remove outlier with preserving the correct point by im-proving the Equal Error Rate (EER) in recognition systems. In palm vein recog-nition system, higher wavelength spectrum of palm vein image will achieved higher verification rate. This can be shows that vein pattern are able and success-fully extract on the image with higher wavelength spectrum

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins