Developing a New Algorithm to Detect Right Thumb Fingernail in Healthy Human

Due to significant challenges faced by traditional methods of personal identification like fingerprinting, eye scanning, and voice recognition, new techniques are needed. One such approach involves the use of human nail images for identification and access to personal identification programs and electronic patient files. A novel algorithm, which consists of three stages, has been proposed utilizing the HSV color space detection algorithm, grayscale contrast optimization algorithm, nail segmentation, and image smoothing with a Gaussian filter. This method reduces tested image data and preserves the primary image structure, and has the potential to surpass the accuracy of traditional methods, providing an additional layer of security in personal identification programs and electronic patient files. Nail image detection can be conducted remotely and accessed through standard cameras or smartphones, making it a more hygienic and convenient option than physical contact methods such as fingerprinting or eye scanning. Moreover, the use of nail images for personal identification has several other benefits, especially in situations where traditional methods are not feasible, such as in individuals with skin conditions that prevent fingerprinting. The success of the proposed algorithm in detecting nail images for personal identification has implications beyond individual security and can be applied in different fields, including healthcare and forensic science, to improve identification accuracy and prevent fraud. For example, the use of nail images could help prevent identity theft in healthcare settings, where sensitive information is stored and exchanged

A comparison of thermographic characteristics of the hands and wrists of rheumatoid arthritis patients and healthy controls

Thermal imaging has been applied to detect possible temperature variations in various rheumatic disorders. This study sought to determine whether rheumatoid arthritis (RA) patients without active synovitis in their hands exhibit different baseline thermographic patterns of the fingers and palms when compared to healthy individuals. Data from 31 RA patients were compared to that of 51 healthy controls. The RA patients were recruited upon confirmed absence of synovitis by clinical examination and musculoskeletal ultrasound. Participants underwent medical infrared imaging of the regions of interest (ROIs). Significant differences were found between the mean temperatures of the palm regions (29.37 °C (SD2.2); n = 306) and fingers (27.16 °C (SD3.2); n = 510) of the healthy participants when compared to the palm regions (31.4(SD1.84)°C; n = 186) and fingers (30.22 °C (SD2.4); n = 299) of their RA counterparts (p = 0.001), with the latter group exhibiting higher temperatures in all ROIs. Logistic regression models confirm that both palm and finger temperature increase significantly in RA without active inflammation. These innovative findings provide evidence that baseline thermal data in RA differs significantly from healthy individuals. Thermal imaging may have the potential to become an adjunct assessment method of disease activity in patients with RA.peer-reviewe

Celebration Schedule 2015 (Friday)

Full presentation schedule for Celebration, Friday, May 1, 201

Efficient Descriptor of Histogram of Ridges Orientation Delineate for Fingernail

Fingernails structure are rich in orientation, ridges and edge features. Inspired by Edge Histogram Descriptor (EHD), this paper presents an efficient orientation-based local descriptor, named histogram of ridges orientation delineate (HROD). HROD is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HROD algorithm first execute and perform a pre-process i.e., re-sizing, filtering, enhancement, segmentation, edge detection and feature extraction. Then, finds oriented edge maps according to predefined orientations using a well-known edge operator mask (2×2 sub block) and obtains a ridges orientation delineate map by choosing an orientation with the maximum edge magnitude for each pixel. In the experiment on this research, five oriented edge maps were used to generate and detect the maximum edge orientation construction of each block, namely vertical, horizontal, diagonal 45°, diagonal 135° and isotropic (non-orientation specific) orientation. Experimental results on fingernail images show that the performance of HROD comparable with the state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HROD algorithm has advantages of low feature dimensionality and fast implementation for a real-time fingernails orientation recognition system.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved

Machine Learning Methods for Human Identification from Dorsal Hand Images

Person identification is a process that uniquely identifies an individual based on physical or behavioural traits. This study investigates methods for the analysis of images of the human hand, focusing on their uniqueness and potential use for human identification. The human hand has significant and distinctive characteristics, and is highly complex and interesting, yet it has not been explored in much detail, particularly in the context of the contemporary high level of digitalisation and, more specifically, the advances in artificial intelligence (AI), machine learning (ML) and computer vision (CV). This research area is highly multi-disciplinary, involving anatomists, anthropologists, bioinformaticians, image analysts and, increasingly, computer scientists. A growing pool of advanced methods based on AI, ML and CV can benefit and relate directly to a better representation of the human hand in computer analysis. Historically, the research methods in this area relied on ‘handcrafted’ features such as the local binary pattern (LBP) and histogram of gradient (HOG) extraction, which necessitated human intervention. However, such approaches struggle to encode the human hand in variable conditions effectively, because of the change in camera viewpoint, hand pose, rotation, image quality, and self-occlusion. Thus, their performance is limited. Recently, there has been a surge of interest in deep learning neural network (DLNN) approaches, specifically convolutional neural networks (CNNs), due to the highly accurate results achieved in many applications and the wide availability of images. This work investigates advanced methods based on ML and DLNN for segmenting hand images with various rotation changes into different patches (e.g., knuckles and fingernails). The thesis focuses on developing ML methods like pre-trained CNN models on the 'ImageNet' dataset to learn the underlying structure of the human hand by extracting robust features from hand images with diverse conditions of rotation, and image quality. Also, this study investigates fine-tuning the pre-trained models of DLNN on subsets of hand images, as well as using various similarity metrics to find the best match of the individual’s hand. Furthermore, this work explores different types of ensemble learning or fusions, those of different region and similarity metrics to improve human identification results. This thesis also presents a study of a Siamese network on sub-images or segments of human dorsal hands for identification tasks. All presented approaches are compared with the state-of-the-art methods. This study advances the understanding of variations in and the uniqueness of humans using patches of their hands (e.g., different types of knuckles and fingernails). Lastly, it compares the matching performances of the left- and right-hand patches using various hand datasets and investigates whether the fingernail produces better identification results than the knuckles. This research shows that the proposed framework for person identification based on hand components led to better person identification results. The framework consists of vital feature extractions based on deep learning neural network (DLNN) and similarity metrics. These elements enhanced the performance. Also, the fingernails' shape performed better than other hand components, including the base, major, and minor knuckles. The left hand can be more distinguishable to individuals than the right hand. The fine-tuning of the hand components and ensemble learning improved the identification results

Automated Person Identification Framework Based on Fingernails and Dorsal Knuckle Patterns

Hand images are of paramount importance within critical domains like security and criminal investigation. They can sometimes be the only available evidence of an offender’s identity at a crime scene. Approaches to person identification that consider the human hand as a complex object composed of many components are rare. The approach proposed in this paper fills this gap, making use of knuckle creases and fingernail information. It introduces a framework for automatic person identification that includes localisation of the regions of interest within hand images, recognition of the detected components, segmentation of the region of interest using bounding boxes, and similarity matching between a query image and a library of available images. The following hand components are considered: i) the metacarpohalangeal, commonly known as base knuckle; ii) the proximal interphalangeal joint commonly known as major knuckle; iii) distal interphalangeal joint, commonly known as minor knuckle; iv) the interphalangeal joint, commonly known as thumb’s knuckle, and v) the fingernails. A key element of the proposed framework is the similarity matching and an important role for it is played by the feature extraction. In this paper, we exploit end-to-end deep convolutional neural networks to extract discriminative high-level abstract features. We further use BrayCurtis (BC) similarity for the matching process. We validated the proposed approach on well-known benchmarks, the ’11k Hands’ dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images known as ’PolyU HD’. We found that the results indicate that the knuckle patterns and fingernails play a significant role in the person identification. The results from the 11K dataset indicate that the results for the left hand are better than the results for the right hand. In both datasets, the fingernails produced consistently higher identification results than other hand components, with a rank-1 score of 93.65% on the ring finger of the left hand for the ’11k Hands’ dataset and rank-1 score of 93.81% for the thumb from the ’PolyU HD’ dataset

Measurement of grip force and evaluation of its role in a golf shot

This study was conducted with the aim of establishing a method to measure time-varying forces at multiple locations at the hand-grip interface, using this method to record how golfers of varying abilities grip the club during a standard tee shot and investigating a potential link between the variations in vibration seen at the grip and the grip force applied near impact. It is hoped that additional knowledge about grip force during a golf shot will lead to improved training techniques and grip design in the future. An assortment of technologies were available for the measurement of grip force, but thinflexible sensors were chosen as they could be applied to the grip or gloves without altering the characteristics of the club. Reliability and performance for these sensors were not well established and, therefore, a novel set of tests were developed to evaluate their capabilities. Thin-film force sensor performance was examined under controlled laboratory conditions to give an indication of each sensor's quasi-static accuracy, hysteresis, repeatability and drift errors, dynamic accuracy and drift errors, and the effects of shear loads and surface curvature. With this newly developed set of tests, five varieties of thin-film force sensor utilizing four different technologies were assessed. The sensors had varying levels of success under the controlled conditions of the evaluation tests. Three of the sensors performed well under static and quasi-static loading conditions, with accuracy errors of 10% or less, hysteresis errors near 6%, repeatability near 6% or below, and drift at 60 s after load application under 15%. Two of these sensors were further tested and demonstrated little change in sensor output to loads applied over curved surfaces, although shear sensitivity and dynamic accuracy errors were more substantial. It was also found that some of the sensors lost sensitivity with repeated loading. Even with these drawbacks, the potential of these sensors to provide useful grip force information was clear. With an understanding of sensor performance in controlled laboratory settings, one sensor type was used to determine regions of peak pressure at the hand-grip interface and three others were used in player tests to obtain time-varying measurements of grip force during a swing. During the player tests, grip force was measured for 10-12 tee shots and impact time was determined Total force was computed for each shot taken by summing the force output of all the sensing elements positioned on either the grip or gloves. When these total force traces were aligned by impact and plotted for each of the golfers tested, an interesting and previously unreported phenomenon became apparent. Each player appeared to have their own grip force 'signature', i.e. total grip force for a particular golfer was very repeatable, but varied considerably between golfers. A grip force signature existed for all players tested regardless of ability, and the level of consistency for an individual golfer and the similarities between golfers was analysed using a cross correlation. It was found that nearly all of the golfers tested had swings that were dominated by the left hand, and that the most notable contributions of the right hand occurred after impact. Variations in grip force were also related to key phases of the swing using high speed video footage. Previously it has been noted that for the same ball, club, and impact location that the vibration on the shaft is remarkably consistent for many different golfers but there is a much greater variation in the vibration at the grip. It was hypothesized that the way a golfer grips the club affects the way vibration is transmitted into their hands and arms. A final set of player tests was therefore conducted with the aim of identifying how grip force affects vibration transmission from the shaft to the hands and the players' perceptions of this vibration. Vibration was measured on the shaft just below the grip and on the golfer'S left thumbnail, force was monitored at 18 locations on the hands, and impact location and clubhead speed were recorded. Each golfer's perceptions of the vibration caused by impact were also noted for two standard drivers. It was found that changes in the amount of vibration travelling from the shaft into the hands is affected by the grip force applied by the golfer. This is the first study to analyse the effects of grip force on vibration transmission into the hands and arms due to a golf impact