Finger Image Research Articles

Identifying Patients with Colon Neoplasias with Gas Discharge Visualization Technique.

To perform an initial assessment of the potential of using the gas discharge visualization (GDV) technique to identify patients with colon neoplasias. The GDV camera (also known as the electrophotonic imaging camera) was used to assess the participants. Colonoscopy was performed on all 78 participants, followed by a GDV scan. The control group consisted of 22 people. An endoscopic examination identified colon tumors in the remaining 56 participants. Participant ages ranged from 45 to 86 years (mean, 64.6 ± 1.2 years). The study analyzed GDV images of each patient's fingers, presenting a whole-body view, as well as separate sectors corresponding to the organs in question. There was a significant number of differences between the control group and the patients with colon tumors. The dynamic of the parameters was examined as the level of tumor dysplasia (neoplasia) varied. The values of the following parameters decreased in the control group as compared to the patients with cancerous polyps: normalized luminescence area, internal noise, contour radius, and average luminescence intensity. The values of the following parameters increased in the control group: radius of the inscribed circle, contour line length, area of luminescence, contour line fractality, contour line entropy, and form coefficients. This pilot study demonstrated a statistical difference between the GDV parameters of patients with colon tumors and the control group. These findings warrant a more in-depth study of the potential for GDV technique in screening programs.

Método Assistido por Computador e Prototipagem Rápida para o Fabrico Personalizado de uma Prótese de Dedo em Banda de Silicone

The fabrication of digital prostheses has acquired growing importance not only for the possibility for the patient to overcome psychosocial trauma but also to promote grip functionality. An application method of three dimensional-computer-aided design technologies for the production of passive prostheses is presented by means of a fifth finger amputee clinical case following bilateral hand replantation.Three-dimensional-computerized tomography was used for the collection of anthropometric images of the hands. Computer-aided design techniques were used to develop the digital file-based prosthesis from the reconstruction images by inversion and superimposing the contra-lateral finger images. The rapid prototyping manufacturing method was used for the production of a silicone bandage prosthesis prototype. This approach replaces the traditional manual method by a virtual method that is basis for the optimization of a high speed, accurate and innovative process.

Design of 128x8 Pixel Array Fingerprint Sensor with a Capacitive-Sensing Circuit Technique for Image Enhancement

This paper proposes 128x8 array swipe type fingerprint sensor with a capacitive-sensing technique for image enhancement. The circuit of one pixel includes a pixel level chargesharing and charge pump to replace an ADC. The circuit also adopts the circuit technique that improves quality of dry finger image captured with capacitive fingerprint sensor LSIs. The proper operation is validated by HSPICE for one-pixel and RTL simulation including logic synthesis for a full chip design on condition of 0.35μm typical CMOS process and 3.3V power. The layout is performed by full custom flow for one-pixel and auto P&R for a full chip. The area of a full chip is 0.161mm 2 (9013μm x 1781μm) and the gate count is 303,329. The area of one-pixel is 58 x 58 μm 2 . Pitch is 60 μm and image resolution is 423dpi.

The biometric recognition on contactless multi-spectrum finger images

This paper presents a novel multimodal biometric system based on contactless multi-spectrum finger images, which aims to deal with the limitations of unimodal biometrics. The chief merits of the system are the richness of the permissible texture and the ease of data access. We constructed a multi-spectrum instrument to simultaneously acquire three different types of biometrics from a finger: contactless fingerprint, finger vein, and knuckleprint. On the basis of the samples with these characteristics, a moderate database was built for the evaluation of our system. Considering the real-time requirements and the respectivecharacteristics of the three biometrics, the block local binary patterns algorithm was used to extract features and match for the fingerprints and finger veins, while the Oriented FAST and Rotated BRIEF algorithm was applied for knuckleprints. Finally, score-level fusion was performed on the matching results from the aforementioned three types of biometrics. The experiments showed that our proposed multimodal biometric recognition system achieves an equal error rate of 0.109%, which is 88.9%, 94.6%, and 89.7% lower than the individual fingerprint, knuckleprint, and finger vein recognitions, respectively. Nevertheless, our proposed system also satisfies the real-time requirements of the applications.

Real-time optoacoustic tomography of indocyanine green perfusion and oxygenation parameters in human finger vasculature.

We interrogated whether optoacoustic tomography could be employed to study blood functional parameters and biodistribution of injected fluorescent agents in humans. Using a multichannel scanner at a frame rate of 10 images per second, we obtained cross-sectional images of the human finger in real time, before and after the administration of indocyanine green. We demonstrated that multispectral optoacoustic tomography can sense fast flow kinetics and resolve spatiotemporal characteristics of a common fluorochrome in human vasculature at clinically relevant concentrations. We further register ICG images with oxygen saturation maps and anatomical views of the proximal interphalangeal joint of a healthy volunteer.

In-Air finger motion interface for mobile devices with vibration feedback

In this paper, we propose the ‘in-air finger motion interface’, which solves a problem users face in interacting with mobile devices. The interface uses only a single small camera to track the three-dimensional (3D) position of the user's finger. The finger region is robustly extracted by using flashing infrared light-emitting diodes (LEDs) and by obtaining the difference image between an illuminated image and a nonilluminated image. The movement of the finger is modeled by transformation of a finger image, and its parameters are accurately estimated by iterative template matching. As a result of the evaluation, an operating area 131% larger than the size of the display and accuracy of input action of 0.6 mm in the x-direction are realized. Finally, to investigate the effect of vibration feedback, we conducted a study and found that vibration feedback significantly reduces the average time to finish the task as well as the number of input errors. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A new approach for inner-knuckle-print recognition

ObjectiveThis paper proposed a new approach for inner-knuckle-print (IKP) recognition. In traditional IKP recognition systems, the region of interest (ROI) is extracted from the image of the whole hand and the directions of the fingers being imaged are not restricted. The result maybe incorrect because that the shape and surface of the fingers may vary greatly. Moreover, if the direction of the finger being imaged is not restricted, there may be severe rotation transform between intra-class IKPs. To overcome these drawbacks, we develop a new data acquisition scheme as well as an efficient personal authentication algorithm. MethodsThe new scheme is designed to capture the image of the inner surface of the middle knuckles of the middle and ring fingers. The fingers being imaged are kept horizontal with two pegs, so that the rotation angle between different images obtained from the same hand can be minimized. The new personal authentication algorithm consists of the next four steps. Firstly, two regions of interest (ROI), each of which contains the inner surface of a knuckle, are cropped from the original image. Secondly, line features are extracted from the ROIs based on the combination of Gabor filtering and derivative line detection method. Then, binary line images are matched by using a cross-correlation-based method. Finally, the input data is classified through score level fusion. ResultsTo evaluate the proposed IKP recognition system, a finger image database which includes 2000 images from 100 volunteers is established. The images are captured on two separate occasions, at an interval of around two months. Most of the volunteers are not familiar with the image acquisition process. The experimental results show that the proposed system achieves high recognition rate and it works in real time. Moreover, the proposed line feature extraction method outperforms traditional Gabor filter based line detection method and derivative line detection method in accuracy. ConclusionThe proposed IKP system is robust and accurate. It may promote the application and popularization of IKP recognition.

Finger vein segmentation in infrared images using supervised and unsupervised clustering algorithms

In this paper, two new methods to segment infrared images of finger in order to perform the finger vein pattern extraction task are presented. In the first, the widespread known and used K nearest neighbor (KNN) classifier, which is a very effective supervised method for clustering data sets, is used. In the second, a novel clustering algorithm named nearest neighbor clustering algorithm (NNCA), which is unsupervised and has been recently proposed for retinal vessel segmentation, is used. As feature vectors for the classification process in both cases two features are used: the multidirectional response of a matched filter and the minimum eigenvalue of the Hessian matrix. The response of the multidirectional filter is essential for robust classification because offers a distinction between vein-like and edge-like structures while Hessian based approaches cannot offer this. The two algorithms, as the experimental results show, perform well with the NNCA has the advantage that is unsupervised and thus can be used for full automatic finger vein pattern extraction. It is also worth to note that the proposed vector, composed only of two features, is the simplest feature set which has proposed in the literature until now and results in a performance comparable with others that use a vector with much larger size (31 features). NNCA evaluated also quantitatively on a database which contains artificial images of finger and achieved the segmentation rates: 0.88 sensitivity, 0.80 specificity and 0.82 accuracy.

New design of finger-vein identification algorithm against image rotation

In order to overcome the impacts of image rotation on the accuracy of the finger-vein identification system,in the image pre-processing module,a new rotation approach based on the fingertip position to correct the target region was presented in this paper.The other part of the proposed system consists of an improved feature extraction module using direction templates and local dynamic threshold segmentation and a classification module using a Modified Hausdorff Distance(MHD).The experimental results show that it achieves a high recognition rate of 97.25%,and the Equal Error Rate(EER) is 0.75% when the drift angle among the images of the same finger is less than 20 degrees.What is more,the whole process took only 161.694 9 ms in VC + +6.0,which meant that the system had a superior real-time performance.The proposed system also has certain practical significance for developing finger-vein identification products.

Phase registration based on matching of phase distribution characteristics and its application in FDOCT

Phase fluctuations in a two-transverse-dimensional scanning Fourier domain optical coherence tomography (FDOCT) seriously affect in vivo phase related applications. The phase difference between two A-scans sampled at the same scanning position or adjacent scanning position is acquired by matching of the phase distribution characteristics on the surface of two A-scans. Finger and palm scanning experiments are performed and defocused images of finger and palm are recovered based on Fresnel scalar diffraction algorithm by using phase compensated OCT complex signals. To further prove the performance of the proposed method, human eye scanning experiments are also performed and blood flow images of retina are extracted from the phase registration results. The accurate, fast and simple phase compensation method is critical for in vivo phase related applications.

A Region Growing Method Based on Statistical Attributes of Infrared Images for Finger Vein Pattern Extraction

In this paper a method for finger vein pattern extraction from infrared images of human finger is proposed. The finger vein pattern is extracted by the execution of a two-step region growing procedure, based on statistical properties of derivatives of the acquired infrared images. Initially, original image is filtered by four different Gaussian kernels (in order to take into account the different orientations of veins). Afterwards, the second partial derivatives of the obtained images are computed. Sequentially, the Hessian matrix of these images is constructed and its eigenvalues are computed in a pixel by pixel basis. The minimum eigenvalue and the absolute value of its gradient comprise the two characteristics (features) used in the two step region growing procedure which follows. The region growing procedure is restricted by statistical attributes such as the mean value and the standard deviation of the segmented regions (vein and tissue) and the mean value and the standard deviation of the gradient of the minimum eigenvalue image. Due to the occurrence of some misclassifications a final post processing step, based on morphological operations, is performed. The developed method achieves to efficiently segment the image despite of intensity variations which are evident in the original image. Moreover, an improved version of the proposed method, which uses the multidirectional response of a specially designed matched filter and its gradient as the two features used in the two stage region growing procedure, is also presented. The modified version, as experimental results show, outperforms the classic version and leads to more robust finger vein pattern extraction.

Multi-spectral bands optical coherence tomography assisted by ultrawide band photonic crystal fiber splitter

We report fabrication and performance of the 2×2 photonic crystal fiber (PCF) splitter that enables multi-spectral bands operation of an optical coherence tomography (OCT) system. The PCF splitter was made by coupling PCFs to a planar lightwave circuit (PLC) splitter chip, which worked as a single mode splitter from visible to near infrared wavelengths. The core size of the PLC splitter chip was about 3μm×3μm and the core-cladding index difference was about 0.25%, which gave the cutoff wavelength at 630nm. The implemented PCF-PLC splitter showed a small excess loss of 1.2∼3.3dB and a low polarization-dependent loss of 0.15∼0.19dB over a wide wavelength range from 640nm to 1000nm. With the splitter, OCT images of a human finger and a nail have been successfully obtained at several spectral bands; 680nm, 840nm, and 930nm center wavelengths.

Adaptive Speckle Reduction in OCT Volume Data Based on Block-Matching and 3-D Filtering

An adaptive speckle denoising method called Volume-BM3D is developed for optical coherence tomography (OCT) volume data processing, including an adaptive noise level estimation algorithm and a denoising strategy based on block-matching and filtering in a highly sparse local 3-D transform domain. Noise level estimation, an important step for denoising, is obtained from estimating signal fluctuations among neighboring A-Scans. Unlike 2-D denoising approaches, the Volume-BM3D considers the similarities of tissue structures either in a single 2-D image or in a volume data simultaneously. It not only effectively suppresses speckle noise, but also improves the visualization of small morphological features, such as sweat glands in finger images. Application of this proposed method to the OCT volume data of a human finger tip shows an 18.4-dB signal-to-noise ratio improvement in speckle noise reduction with little edge blurring. It outperforms other filtering methods in suppressing speckle noises and revealing attenuated subtle features.

Secure Transmission of Biometric Scan Images Using Data Encryption Standards(DES) Algorithm

Bio metric Scan Images, however, is vulnerable to unauthorized access while in storage and during transmission over a network. Unauthorized user can make a fake copy fro m image file fro m memory device o r co mputer. Bio metric Data in form of images are very confidential informat ion of user. If they are in unauthorized hand then misuse of data may be harmful to authorized user. As in case of Un ique ID each and every citizen of country have their Bio metric scan images of finger, face and thumb and Iris all in original form like JPEG,JPG,GIF,PNG,BMP etc. To protect them fro m unauthorized access we can encrypt images so they can't be detected and used. Protection is required at very init ial end so that images can be directly saved in form of encrypted image in memory device. First image will be converted into pixels. Each pixel will be converted into DataStream and stored in codebook in same order as of p ixels in image. Digitized p ixels will have a group of data bits and each pixels or set of pixels will be encrypted using DES algorith m. The whole Programming and design can be done in any platfo rm like MATLA B, C, C++, Xilin x ISE 13.4 Soft ware. The advantage of using Xilin x is that in this platform we can design a Layout of it so that finally a Ch ip level So lution can be provided.

Microscopic Degradation Analysis of RF-Stressed AlGaN/GaN HEMTs

An AlGaN/GaN high electron mobility transistor (HEMT) stressed at 10 GHz and increased channel temperatures of T ≈ 260 °C has been analyzed by electroluminescence microscopy (ELM) and infrared thermography (IRT). After stress a negative threshold shift is seen in the electrical characteristics. Based on the current dependence of the electroluminescence (EL) intensity image and a local increase of T this shift can be assigned to the degradation of one of its gate fingers. Transmission electron microscopy (TEM) images of this gate finger revealed structural changes along the drain-side edge of the gate.

Blind retrospective shading correction using a multi-objective minimization criterion

In this paper, a fully automatic blind retrospective shading correction method based mainly on a minimization of a multi-objective criterion is presented. The proposed method assumes that the acquired image has distorted from a multiplicative and an additive shading component and thus can be adequately described by the linear image formation model. The estimation of the shading-free image is based on parametric estimation of the multiplicative and the additive shading component and the consequent application of the inverse image formation model. First of all, an initial estimation of the shading-free image is performed by the minimization of the multi-objective function of an appropriate image criterion. Secondly, the multiplicative and the additive shading components are estimated, based on assumptions about their frequency content and then, they median filtered. Finally, an estimation of a shading-free image is obtained using the above estimations for the components and the application of the inverse image formation model.Qualitative and quantitative experiments were conducted in a variety of image modalities including artificial and real images of finger, retinal images, transmission electron microscopy (TEM) images, arm, palm and hand vein images and thorax X-ray images. In all cases of distorted images, the proposed method had successfully removed the majority of the shading effects and had not distorted the shading-free images satisfying the main goal of retrospective shading correction. The signal to noise ratio (SNR) or equivalently the reciprocal of the coefficient of variations is used as a quantitative measure of the reduction/increase of intensity variations within the objects of the same class after shading correction. In our experiments, for the purpose of evaluation the signal to noise ratio is calculated for two different classes (object and background).

Selection of convolution kernel in non-uniform fast Fourier transform for Fourier domain optical coherence tomography

Gridding based non-uniform fast Fourier transform (NUFFT) has recently been shown as an efficient method of processing non-linearly sampled data from Fourier-domain optical coherence tomography (FD-OCT). This method requires selecting design parameters, such as kernel function type, oversampling ratio and kernel width, to balance between computational complexity and accuracy. The Kaiser-Bessel (KB) and Gaussian kernels have been used independently on the NUFFT algorithm for FD-OCT. This paper compares the reconstruction error and speed for the optimization of these design parameters and justifies particular kernel choice for FD-OCT applications. It is found that for on-the-fly computation of the kernel function, the simpler Gaussian function offers a better accuracy-speed tradeoff. The KB kernel, however, is a better choice in the pre-computed kernel mode of NUFFT, in which the processing speed is no longer dependent on the kernel function type. Finally, the algorithm is used to reconstruct in-vivo images of a human finger at a camera limited 50k A-line/s.

A finger-vein verification system using mean curvature

The finger-vein pattern is one of the human biometric signatures that can be used for personal verification. The first task of a verification process using finger-vein patterns is extracting the pattern from an infrared finger image. As a robust extraction method, we propose the mean curvature method, which views the vein image as a geometric shape and finds the valley-like structures with negative mean curvatures. When the matched pixel ratio is used in matching vein patterns, experimental results show that, while maintaining low complexity, the proposed method achieves 0.25% equal error rate, which is significantly lower than what existing methods can achieve.

A Novel Method of Constructing Human Middle Finger from its Fractional Part

The human hand is a masterpiece of mechanical complexity. The anatomy of the hand is complex, intricate, and fascinating. Its integrity is absolutely essential for everyday functional living. Hands may be affected by many disorders, most commonly traumatic injury. In treating hand problems, the mastery of anatomy is fundamental in order to provide the best quality of care. In this paper, the focus is on the construction of middle finger of the hand when only fractional part of the finger tip image is available. 24 Geometric features of both the hands from 100 people of different age group were extracted from the silhouettes. From the known middle finger width, the proposed method can be used to estimate middle finger length, position of knuckles and also finger width at the second knuckle using taalamana system and shilpa shastra. 600 images of various age groups were collected and from the fractional part of the middle finger image, complete middle finger is constructed based on the estimated values of finger length and width. The estimation accuracy of more than 91% is achieved for all the estimated features. Our construction algorithm efficiently works for various cases like fingers with nail, fingers of left or right hand and fingers with high degree of variation in color and age.

New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%.