Palm Images Research Articles

Identification Of Palm Using Otsu Method and Mathematical Morphology to Open House Doors Identifikasi Telapak Tangan Menggunakan Metode Otsu Dan MorphologynMatematika Untuk Membuka Pintu Rumah

The biometrics system is an individual recognition technology using human body parts or behavior. Palm and fingerprints, each biometrics example based on body parts and human behavior. Image matching is a way to identify images. This research uses biometric technology, a system that performs image matching based on human body parts by matching test images received with training images contained in a database. The image used is the palm of the hand. The use of the palm because the palm has unique characteristics, is difficult to fake and tends to be stable. There are 10 samples of the palm image that will be used using the Otsu method and the Math Morphology method, which were previously filtered using a median filter and turned into grayscale. From 10 different samples showing different results with an average similarity level of 96.28%, the method proves that the palm joints are a characteristic that is owned by everyone, and it also proves that the palm can also used as an alternative to protect privacy, not just fingerprints for home security.

Palm Vein Detection Based on Local Gabor Vein Code Pattern and Hog

Nowadays, Palm vein biometric is one of the emerging techniques to build up a well-organized recognition system. This paper focuses on quality-based palm vein detection. To recognize hand palm vein images, Local Gabor Vein Code Pattern (LGVCP) is proposed. It comprises of three modules, namely Preprocessing, LGVCP and Palm Vein matching. Preprocessing is performed using Otsu thresholding method and Contrast Limited Adaptive Histogram Equalization (CLAHE) techniques. Palm vein features are extracted by applying LGVCP and Histogram of Oriented Gradients (HOG). Chi-square distance and Euclidian distance values are applied for palm vein matching. Experiments are performed on CASIA Palm vein Image database. The performance of the proposed method is evaluated by metrics, namely accuracy, specificity, sensitivity, error rate, recall and precision. The proposed method provides an accuracy of 99.001% compared to the existing ones.

Research on Feature Extraction Algorithm of Palm Main Line Based on Bottom Hat Transformation

Aiming at the problem that the main line of palm is not easy to extract, and it is easy to be affected by illumination and uneven color of palm, this paper proposes a feature extraction algorithm based on base The bottom transformation. The bottom cap transform can eliminate the influence of uneven illumination and extract the main line of the palm. The collected palm image is first grayscaled and bottomed, and then binarized, the palm area is separated, the palm area is found, the palm boundary is separated by the Sobel edge detector, and the fingertip valley coordinates are determined. The palm area is divided, the knuckles and palm prints are found, and the vertical projection is used to match the palm print and the knuckle pattern. The experimental results show that the method can eliminate the influence of uneven illumination and uneven color of palm, and it has fast speed and strong anti-noise ability. It is suitable for large-scale palm database to achieve coarse screening.

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

AbstractThe impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D2LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).

New Algorithm for Palm Print Detection

Advances in technology are increasing the demand for the biometric technology of palm print detection. This work presents a region of interest segmentation algorithm for palm prints based on Hue Saturation Brightness. The aim of this work was to detect palm print region of interest based on skin color and geometric features. After a color palm image is read as a JPEG file, the algorithm passes through three main stages of preprocessing: palm localization, region of interest localization and region of interest extraction. The preprocessing stage consists of three steps: conversion to Hue Saturation Brightness, skin color modeling, and de-noising. The second main stage of palm localization consists of clipping from the left, from the right, from the top and from bottom. The third stage consists of the two steps of determining and extracting region of interest. The algorithm was tested on a dataset of 180 palm images from the Institute of Automation Chinese Academy of Sciences dataset, which contains an equal number of right-hand and left-hand palm images. The results showed that the Hue Saturation Brightness color space for skin detection was superior to other recognition methods, with an accuracy rate of 98.8%.

Far-red photoactivatable BODIPYs for the super-resolution imaging of live cells.

The identification of viable designs to construct switchable fluorescent probes and operate them in the interior of live cells is essential to allow the acquisition of SMLM images and permit the visualization of cellular components with sub-diffraction resolution. Our laboratories developed a mechanism to switch the fluorescence of BODIPY chromophores with the photoinduced cleavage of oxazine heterocycles under mild 405-nm illumination. With appropriate structural modifications, these switchable molecules can be engineered to immobilize covalently on large biomolecules within lysosomal compartments of live COS-7 cells and produce bright far-red fluorescence with optimal contrast upon activation. Such a combination of properties permits the acquisition of PALM images of the labeled organelles with localization precision of ca. 15nm. This article reports the experimental protocols for the synthesis of and live-cell labeling with these compounds as well as for the reconstruction of super-resolution images of the resulting biological preparations.

Verifikasi Telapak Tangan dengan Multimodal Biometrik Fermat’s Spiral

The use of biometrics in various issues of data security, transactions, privacy has been widely used. Regarding security, surely the level of accuracy in one's self-recognition must be accurate. The palmprint is one of biometrics which has various unique characteristics. These characteristics include geometrical features such as length, width, and area of the palmprint, characteristics of main lines such as heart lines, headlines, and lifelines, tangled or weak lines, delta dot characteristics, and minus characteristics. Main lines and tangled lines, which are often referred to as line features, have several advantages over other biometric features, such as they can be obtained from low-resolution images, are difficult to duplicate, and are stable because they have experienced slight changes over a long period of time. This research introduces the method of Fermat's Spirals in the extraction of the features of the palmprint which are represented in the time series curve. Testing uses 450 palm images sourced from 50 people, each person taken 9 palmprint images where 5 images as a test image and 4 images as a reference image. The test is carried out based on the threshold value determined from the simulation results based on matching test images with reference images on the ROC graph. Based on the test results it can be concluded that the system shows high performance at a threshold value of 0.036 to get an FNMR value of 2.80%, FMR of 0.88% with an accuracy of 96.31% where the number of reference images to the test image affects the level of accuracy.

Regenerating face images from multi-spectral palm images using multiple fusion methods

This paper established a relationship between multi-spectral palm images and a face image based on multiple fusion methods. The first fusion method to be considered is a feature extraction between different multi-spectral palm images, where multi-spectral CASIA database was used. The second fusion method to be considered is a score fusion between two parts of an output face image. Our method suggests that both right and left hands are used, and that each hand aims to produce a significant part of a face image by using a Multi-Layer Perceptron (MLP) network. This will lead to the second fusion part to reconstruct the full-face image, in order to examine its appearance. This topology provided interesting results of Equal Error Rate (EER) equal to 1.99%.

Individuals Identification Based on Palm Vein Matching under a Parallel Environment

Biometric identification and verification are essential mechanisms in modern society. Palm vein recognition is an emerging biometric technique, which has several advantages, especially in terms of security against forgery. Contactless palm vein systems are more suitable for real-world applications, but two of the major challenges of the state-of-the-art contributions are image deformations and time efficiency. In the present work, we propose a new method for palm vein recognition by combining DAISY descriptor and the Coarse-to-fine PatchMatch (CPM) algorithm in a parallel matching process. Our proposal aims at providing an effective and efficient technique to obtain similarity of palm vein images considering their displacements as discriminatory information. Extensive evaluation on three publicly available databases demonstrates that the discriminability of the proposed approach reaches the state-of-the-art results while it is considerably superior in time efficiency.

Robust and adaptive algorithm for hyperspectral palmprint region of interest extraction

Recently, hyperspectral imaging has attracted more and more considerable research attention because of its discriminative information. This study proposes a robust approach to adaptively extract the hyperspectral palmprint region of interest (ROI) captured by a hyperspectral palmprint acquisition device, which is considered one of the most important stages in palmprint recognition. For different spectral wavelengths, the image has different illuminations and unbalanced shadows. In particular, mean grey values of palm images in different bands have large variations, such that binarisation of the palm image can be considered a challenging task to accurately separate the contour of the palm from the original image. To solve these problems, this study proposes an adaptive ROI segmentation algorithm, whereby a support vector machine-based method is used to detect the palm from the image and a coordinate established to ensure the accuracy of the ROI. The proposed method has been tested on a hyperspectral palm data set which covers spectrums from 530–1030 nm with 20 nm intervals. The experimental results showed that the proposed algorithm is effective and efficient at locating the ROI in hyperspectral palmprint images, where local binary pattern features were extracted from the ROIs achieving an equal error rate (EER) of 1.49% and an accuracy of 99.51% in recognition.

Finger Edge Contour Perimeter as a Biometric Based Identification System

Generally, personal identification system is still done by conventional techniques, such as using ID cards, passwords or PIN numbers. These systems is easily lost, damaged or forgotten. In this paper we present a new method for identifying a person with a biometric based on palm of the hand. This system does not require a peg like an identification system based on existing hand geometry. Phase of development are: first, the palm image scanned in the RGB format is converted into a gray level. Then the image is converted to binary by giving the correct threshold value. From the binary image, we carry out the edge detection process to get the contour perimeter of the palm. From the contour perimeter of the palm, we measure the length of 8 edges contour perimeter of the fingers i.e. the right edge of the thumb, the left edge of the index finger, the right edge of the index finger, the left edge of middle finger, the right edge of middle finger, the left edge of pinkie, the right edge of pinkie, and the left edge of ring finger. The identification process is performed by minimum distance, which is the smallest euclidean distance value associated with the test image and database. The experimental results giving good performance with a success of up to 90%.

Multispectral Palmprint Recognition Based on Multidirectional Transform

Multispectral palmprint recognition is one of the most useful biometric techniques due to features obtained from different spectral resolutions/wavelengths. In this paper, we propose a multidirectional transform-based feature encoding plan for reliable and robust representation and matching of multispectral palm images. The method extracts the region of interest (ROI) for palmprint images captured with non-contact sensors. The registered ROI of each band is newly downsampled using DWT. This approach allows us to take more lines into consideration for interpolation. A undecimated dual-tree complex wavelet transform based multidirectional feature encoding plan is then newly applied since it provides better shift invariance and directional selectivity. Finally, a binary code matching strategy with score level fusion is used to compute matching for efficient identification. The experimental results obtained on CASIA and PolyU datasets show that the presented method gives better results in the blurring binary code matching case than state-of-the-art methods and provides comparable performance in the non-blurring binary code matching.

Boosting palmprint identification with gender information using DeepNet

Abstract Palmprint has been found to possess distinct features for reliable person identification and gender classification. Traditionally, the palmprint-based recognition approaches often rely on hand-crafted feature descriptors, which do not migrate well to the emerging datasets. This is because the local textural features cannot always be well preserved under complex acquisition environment. In recent years, deep convolutional neural networks (CNNs) have greatly advanced the performance of biometric applications even encountering non-ideal images caused by motion blur, poor contrast or illumination artifacts. However, CNNs have not been fully explored in palmprint-based biometric recognition and gender classification, mainly owing to lack of diverse training data with gender labels. In this paper, our goal is to prove a CNN model that can be successfully used for palmprint identification and gender classification. Also, we tend to prove the hypothesis that gender information can help to boost palmprint identification performance. For this end, we first proposed two palmprint datasets: BJTU_PalmV1 and BJTU_PalmV2 with gender attributes, captured by CCD cameras and mobile phones, respectively. BJTU_PalmV1 contains 2431 hand images from 174 persons, whilst BJTU_PalmV2 contains 2663 hand images from 148 subjects. Secondly, two deep CNN-based models are separately designed to accomplish palmprint identification and gender classification in an end-to-end trainable fashion using palm images. Thirdly, we introduced two novel deep CNN architectures to validate the hypothesis that gender information is conductive to identification accuracy. The experimental results conducted on six different datasets: PolyU2D/3D1.0, IITD, Tongji, CASIA, BJTU_PalmV1 and BJTU_PalmV2, indicate that the proposed approaches achieve high accuracy and strong generalization ability. Also, the experimental results solidify that gender information can help in boosting palmprint identification.

Low‐cost biometric recognition system based on NIR palm vein image

Palm vein recognition is motivated by the advantages of high security and liveness detection, but its popularity is prevented by the cost of palm vein capture devices. This study proposes a low-cost and practical palm vein recognition system. First, the authors' system captures near-infrared (NIR) palm vein image with complementary metal-oxide-semiconductor camera in lieu of an NIR charge-coupled device camera. The goal is to reduce the cost of palm vein capture devices greatly. Second, this study adopts thenar area on the palm as the region of interest (ROI) for further palm vein recognition. The goal is to get the rich vessel and avoid the effect of palmprint. Finally, the discriminate palm vein features are extracted based on Haar-wavelet decomposition and partial least squares algorithm on the ROI image. The goal is to increase the recognition accuracy, though the resolution of the image is low. A database with 1500 palm vein images from 250 samples is setup with the capture device. Experiments in the self-built database and a public database show the effectiveness of the scheme.

Personal authentication based on wrist and palm vein images

One of the newest promising biometrics researched today is the vein pattern recognition. However, little efforts have been invested in this direction. In this paper, two frameworks focused on a palm and wrist vein-based multimodal authentication system are proposed. For the first framework, wrist and palm traits of the same hand are fused, whilst four biometric markers are combined in the second framework using texture descriptors such as local phase quantisation (LPQ), local binary patterns (LBPs), binarised statistical image features (BSIF) and local ternary patterns (LTP). In addition, two approaches of score level fusion are applied: 1) transformation-based using sum rule, min-max rules and t-norms; 2) classifier-based via t-norms. The experimental results on publicly available dataset show that the integration of wrist and palm vein images from both left and right hand gives much improved accuracy than the fusion of two traits of one hand. The recognition rate of the proposed wrist-palm vein based multibiometric system is found to be 100%.

Personal authentication based on wrist and palm vein images

One of the newest promising biometrics researched today is the vein pattern recognition. However, little efforts have been invested in this direction. In this paper, two frameworks focused on a palm and wrist vein-based multimodal authentication system are proposed. For the first framework, wrist and palm traits of the same hand are fused, whilst four biometric markers are combined in the second framework using texture descriptors such as local phase quantisation (LPQ), local binary patterns (LBPs), binarised statistical image features (BSIF) and local ternary patterns (LTP). In addition, two approaches of score level fusion are applied: 1) transformation-based using sum rule, min-max rules and t-norms; 2) classifier-based via t-norms. The experimental results on publicly available dataset show that the integration of wrist and palm vein images from both left and right hand gives much improved accuracy than the fusion of two traits of one hand. The recognition rate of the proposed wrist-palm vein based multibiometric system is found to be 100%.

Date-palm (Phoenix, Arecaceae) iconography in coins from the Mediterranean and West Asia (485 BC–1189 AD)

Abstract Date palm (Phoenix dactylifera L.) yields food, medicines, and materials for buildings and crafts in the Middle East, the Mediterranean and North Africa. There, date palms were represented on coins from 485 BC until 1189 AD. These palm-tree images furnish agricultural, botanical and geographical information on Phoenix. We aim to analyze the evolution over time of the diversity of these images and their relationship with climatic episodes favorable to their cultivation. We selected variables for analysis based on date-palm taxonomy, standardized descriptors and observation of the coins. Image types, realism and age, and relationships with climatic events were analyzed using multivariate techniques. Ten main palm-image types were recognized. Three geographically and chronologically differentiated periods are distinguished associated with favorable climate episodes. First, the western period of Carthage and Sicily (485–100 BC) with highly realistic date palm-tree images, followed by a bottleneck 100 BC–50 AD, a second period with lesser diversity of palm images (50–150 AD) centered in the eastern Mediterranean, and a third medieval period (430–1189 AD) with schematic images of palm trees. From a cultural viewpoint, our results are consistent with ancient texts for morphology, cultivation, management techniques, sterility and fertility, and tree longevity. Several image types from present-day Israel most likely represent specific ancient cultivars such as the “Caryotae”. Ancient coins offer valuable information, to better knowing characteristics of date palm trees and date-palm cultivation, from the first millennium BC to the beginning of second millennium AD. Western Mediterranean presents greatest diversity and realism related to different domestication events.

Performance analysis of biometric recognition system based on palmprint

Nowadays biometric authentication is one of the most preferred choice and in that also palmprint is becoming the most widely accepted technique because it can be captured easily and the algorithms can be implemented with ease. Detection of coarse lines in palm images is easy to the point that these lines can be revealed even by using a low determination camera. The integration of palmprint recognition with some other biometric recognition system does not require any special capture devices, so it can be done easily. Hence, this method suites best for individual verification. This research work is related to palmprint recognition in which the main aim is related to software based performance analysis of classifiers. The entire work is divided into three major phases namely palmprint pre-processing, feature extraction and accuracy estimation. Different functions and operators related to binarization, morphology and other operations are used for pre-processing; similarly feature extraction is carried out by using Frangi filter, Freak descriptor and FAST algorithm. After the final feature extraction procedure, classifiers are used to predict the accuracy in identifying right and wrong features for a given subject. This research work uses two classifiers namely discriminant analysis and K-nearest neighbor algorithm (KNN) where discriminant analysis shows 77% accuracy and KNN gives a better accuracy of 93%.

Optimization of sample preparation and green color imaging using the mNeonGreen fluorescent protein in bacterial cells for photoactivated localization microscopy

mNeonGreen fluorescent protein is capable of photo-switching, hence in principle applicable for super-resolution imaging. However, difficult-to-control blinking kinetics that lead to simultaneous emission of multiple nearby mNeonGreen molecules impedes its use for PALM. Here, we determined the on- and off- switching rate and the influence of illumination power on the simultaneous emission. Increasing illumination power reduces the probability of simultaneous emission, but not enough to generate high quality PALM images. Therefore, we introduce a simple data post-processing step that uses temporal and spatial information of molecule localizations to further reduce artifacts arising from simultaneous emission of nearby emitters. We also systematically evaluated various sample preparation steps to establish an optimized protocol to preserve cellular morphology and fluorescence signal. In summary, we propose a workflow for super-resolution imaging with mNeonGreen based on optimization of sample preparation, data acquisition and simple post-acquisition data processing. Application of our protocol enabled us to resolve the expected double band of bacterial cell division protein DivIVA, and to visualize that the chromosome organization protein ParB organized into sub-clusters instead of the typically observed diffraction-limited foci. We expect that our workflow allows a broad use of mNeonGreen for super-resolution microscopy, which is so far difficult to achieve.

Biometric recognition using finger and palm vein images

In recent times, biometrics is the best alternative for the token-based and knowledge-based security systems. Out of the existing biometric modalities, the vascular biometric modalities are preferred for authenticating the person, because of its uniqueness among all individuals. This paper proposes a multimodal biometric system using vascular patterns of the hand such as finger vein and palm vein images. Initially, the input palm vein and finger vein images are pre-processed so as to make them suitable for further processing. Subsequently, the features from palm and finger vein images are extracted using a modified two-dimensional Gabor filter and a gradient-based techniques. These extracted features are matched using the Euclidean distance metric, and they are fused at the score level using fuzzy logic technique. The proposed technique is tested on the standard databases of finger vein and palm vein images. This method provides lower false acceptance rate, false rejection rate and high accuracy of 99.5% when compared with the existing techniques, indicating the effectiveness of the proposed system.