Technique for Personal Identification: Using the Extracted Index Finger Image to Identify Individuals

This research uses the object extracting technique to extract the index finger image. The algorithm developed in this research can find the precise locations of the different fingers' fingertips and the finger-to-finger-valleys. After finding the positions of the fingertips and finger-valleys, the index finger, middle finger, and ring finger can be extracted separately. The extracted finger-images contain many useful geometric features. One can use these features for personal identification. The orientation of the index finger is found. Image rotating, image shifting, and image interpolating techniques are used to align different persons' index fingers to the same position for comparison. Image subtraction is used to examine the difference between two index finger images. In this research, so far only the index finger has been used as the feature to identify different persons. By using the index finger image, the algorithm developed in this research can very precisely identify different persons.     

Finger surface as a biometric identifier

We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers’ surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.     

非接触成像手指节纹检测算法

指节纹因具有唯一性而成为身份认证的方法之一,现有的文献主要基于小波多分辨率分析原理提取指节纹分布信息,而本文给出了一种针对指节纹间相对位置关系进行检测的方法：首先对自建图库中大拇指向外张开、四指闭合的手掌图像,利用Step边缘检测的预处理方法定位出关键的指尖、指根点,然后将整幅手掌图像旋转至水平位置,以便于比较指节纹的相对位置分布关系,最后针对除大拇指之外的四个手指,利用 Roof 边缘检测的方法确定指节纹特征点.为了验证本文的方法,利用自建小型图库进行测试,结果表明正确检测率可达98.035%,验证了该方法的可行性和有效性.     

基于Kinect深度图像的指尖检测与手势识别

针对基于普通摄像头的手势识别系统在不同光照条件和复杂环境下易受影响的问题,提出一种基于kinect深度图像进行指尖检测和手势识别的算法. 首先利用Kinect传感器获取深度图像,再利用OpenNI手部跟踪器检测出手部的位置,根据手部位置对手势进行深度阈值分割. 提出一种结合凸包和曲率检测指尖的算法,检测出指尖数目和位置后,计算出包括指尖和手掌水平方向的夹角、相邻两个指尖夹角以及指尖与掌心的距离的特征向量,最后利用支持向量机（SVM）对预定的9种数字手势进行识别. 实验邀请5位实验者在复杂环境下每个手势做30次,每次的手势角度不同,实验结果表明该方法能够准确检测出指尖的数目和位置,9种数字手势平均识别率达到97.1%,该方法使用特征简单,实时性好,有较好的鲁棒性.     

Features extraction from hand images based on new detection operators

Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed.     

Chinese-Seal-Print Recognition by Color Image Dilating,Extraction, and Gray Scale Image Geometry Comparison

This research uses the morphology technique to extract the Chinese-seal-print-images. The algorithm developed in this research can find the precise locations of the Chinese-seal-print-images. After finding the positions of the Chinese-seal-print-images. Image rotating, image shifting, and image interpolating techniques are used to align different Chinese-seal-print-images to the same position for comparison. Image subtraction is used to exam the difference of two Chinese-seal-print-images. The extracted Chinese-seal-print-images contain many useful geometry features. By using these geometry features, one can classify different Chinese-seal-print-images.     

Snake模型在医学图像分割中的应用

基于Snake模型的图像分割技术是比较成功的研究领域,也是近年来图像处理领域研究的热点之一。Snake模型将基于图像本身的底层的视觉属性和人们先验知识结合起来,针对各种图像的不同特点,从高层和底层两个方面约束图像分割的过程。通过对Snake模型的基本原理及其几种改进的概括研究,阐述了Snake模型在图像分割中的一些应用方法。     

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.     

Individual finger contribution in submaximal voluntary contraction of gripping

The objective of this study was to evaluate individual finger force and contribution to a gripping force, the difference between actual and expected finger forces and subjective discomfort rating at 10 different submaximal voluntary contraction (%MVC) levels (10–100% in 10 increments). Seventy-two participants randomly exerted gripping force with a multi-finger force measurement system. The individual finger force, gripping force and discomfort increased as %MVC levels increased. The middle and ring fingers exerted more force and contributed to a gripping force more than the index and little fingers due to their larger mass fractions of the digit flexor muscles. It was apparent at <50% MVC; however, the index finger increased its contribution and exerted even more force than expected at more than 50% MVC. Subjective discomfort supported the results of the objective measures. This could explain the conflicting findings between index and ring fingers in previous finger contribution studies.

Statement of Relevance: Hand tool design is of special interest in ergonomics due to its association with musculoskeletal disorders in the hand. This study reveals a different contribution pattern of the fingers in submaximal voluntary contraction of gripping exertion.     

使用方向滤波技术的手指静脉纹路提取方法

为了准确高效地提取手指静脉纹路,提出一种新的基于方向滤波的手指静脉纹路提取方法。该方法结合静脉纹路特点设计手指静脉图像的方向图及方向滤波器,根据所得的方向图及方向滤波器对图像进行滤波增强,对增强后的图像提取手指静脉模式。与传统的二值化方法相比,在进行二值化操作前,先对图像进行方向滤波处理,使得该方法提取出的手指静脉纹路连通性与光滑性好、噪声与伪特征少,且其不仅对高质量图像能够准确有效地提取出静脉纹路,对低质量图像处理效果也比较理想。     

IQBIC系统图像检索方法

基于图像底层特征的检索技术主要利用图像形状、纹理和颜色特征. 不同的图像, 不同的用途一般需要针对不同的特征来选取检索方法, 为此我们考虑先从图像本身的特征分析, 寻找出该图像的较为明显的特征, 然后根据这一分析结果, 再利用合适的特征检索方法去检索该图像, 使得图像检索更接近于智能化. 文章简述了颜色直方图和纹理的累计游程直方图算法, 提出了形状方向能量直方图算法, 并特别说明图像特征的智能选取方法. 基于内容的智能图象检索系统(Intelligent Query By Image Content Sy     

基于指部关联特征的多模态图像采集系统

针对基于单个生物特征的身份认证安全性和稳定性不足的问题,设计了基于指部关联特征的多模态图像采集系统,采用单个双波段摄像头分时采集同一根手指的指纹、指节纹和指静脉图像。指纹和指节纹采用非接触反射采集方式,指静脉采用单侧近红外光源与反射镜面相结合的透射采集方式,并根据静脉图像质量评价动态调控光源,根据特征点信息量动态调整各个特征的权重。实验结果表明,该多模态采集系统在认证通过率、误识率和拒登率等指标都优于指纹或指静脉的单模态采集系统,认证通过率达到99.1%,误识率为0.000 1%,不存在拒登现象。     

基于深度神经网络的图像语句转换方法发展综述

在当前大数据时代,图像由于具有丰富的语义而成为大众获取相关信息的重要来源。基于深度模型的图像语义分析是一种通过深度模型将图像内容转换成可直观理解的语义知识的技术,受到了国内外研究者的广泛关注。该技术根据生成目标语义层次的差异,可分为单类别、多标签和语句3类。首先介绍了以上3类方法对应的深度模型的结构特点,并从技术的演化趋势角度对比分析了3类方法的技术特点和发展现状;然后重点对图像语句转换方法的发展现状、应用场景与性能要求的差异进行了论述,同时对图像语句转换方法的步骤进行分解和论述,从学术界和产业界两方面进行了详细的对比分析,指出了二者的不同研究侧重点与对应的发展现状;最后对具有深度模型的图像语句转换方法进行了总结和展望,指明了该方法当前存在的问题与发展趋势。     

Analysis of natural finger-press motions for design of trackball buttons

This study analysed natural press motions of the index, middle and ring fingers for ergonomic design of the positions and surface angles of the left, middle and right trackball buttons. Finger motions of 26 male participants for naturally pressing the trackball buttons were recorded after the participants adjusted the trackball buttons to their preferred locations for comfortable pressing. The natural positions of the finger pulps formed a symmetrically rainbow-shaped reach zone for the fingers. The natural press angles of the fingers’ motion trajectories to the vertical reference line ranged from 14.2° to 20.5°, suggesting an 18-degree surface from the horizontal line for the trackball buttons. Regression formulas (adjusted R²?=?0.90?±?0.07 and mean squared error?=?8.55?±?7.52?mm) were established to estimate the natural positions of finger pulps from hand segment lengths and joint angles for a population having different hand sizes from this study.

Relevance to industry     

Image super-resolution via two coupled dictionaries and sparse representation

In image processing, the super-resolution (SR) technique has played an important role to perform high-resolution (HR) images from the acquired low-resolution (LR) images. In this paper, a novel technique is proposed that can generate a SR image from a single LR input image. Designed framework can be used in images of different kinds. To reconstruct a HR image, it is necessary to perform an intermediate step, which consists of an initial interpolation; next, the features are extracted from this initial image via convolution operation. Then, the principal component analysis (PCA) is used to reduce information redundancy after features extraction step. Non-overlapping blocks are extracted, and for each block, the sparse representation is performed, which it is later used to recover the HR image. Using the quality objective criteria and subjective visual perception, the proposed technique has been evaluated demonstrating their competitive performance in comparison with state-of-the-art methods.     

一种面向实时交互的变形手势跟踪方法

变形手势跟踪是基于视觉的人机交互研究中的一项重要内容.单摄像头条件下,提出一种新颖的变形手势实时跟踪方法.利用一组2D手势模型替代高维度的3D手模型.首先利用贝叶斯分类器对静态手势进行识别,然后对图像进行手指和指尖定位,通过将图像特征与识别结果进行匹配,实现了跟踪过程的自动初始化.提出将K-means聚类算法与粒子滤波相结合,用于解决多手指跟踪问题中手指互相干扰的问题.跟踪过程中进行跟踪状态检测,实现了自动恢复跟踪及手势模型更新.实验结果表明,该方法可以实现对变形手势快速、准确的连续跟踪,能够满足基于视觉的实时人机交互的要求.     

一种光照鲁棒的图像拼接融合算法

针对传统图像拼接方法只能处理光照一致图像的问题,提出了一种对环境光照鲁棒的全景图拼接算法。该算法首先使用圆环投影来获取待拼接图像的匹配特征序列,不仅克服了传统图像特征提取方法中的区域局限性问题,而且较好地实现了光照变化的图像匹配;然后使用统计参数来调整待拼接图像的整体亮度,以解决光照变化问题;最后对于传统图像融合处理中采用线性加权函数通常引起的最终拼合图像重叠区域模糊问题,构造了包含图像梯度的能量函数,用于计算重叠区域的全局最优融合因子。实验表明,该算法对光照变化图像的拼接融合能取得满意的视觉效果。     

基于内容的图像分类体系

对图像资源的有效使用和管理迫切需要建立图像分类的标准或规范，本文通过采集网络图像样本，考察它们的视觉特征差异情况，提出了基于内容的图像分类体系，并且就每类图像的特点予以介绍，指出了图像中用于分类的鲜明的视觉特征。