Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.     

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.     

Contact-free hand geometry-based identification system

This paper presents an approach for personal identification using hand geometrical features, in which the infrared illumination device is employed to improve the usability of this hand recognition system. In the proposed system, prospective users can place their hand freely in front of the camera without any pegs or templates. Moreover, the proposed system can be widely used under dark environment and complex background scenarios. To achieve better detection accuracy, in total 13 important points are detected from a palm image, and 34 features calculated from these points are used to further recognition. Experimental results demonstrate that the averaged Correct Identification Rate (CIR) is 96.23% and averaged False Accept Rate (FAR) is 1.85%. These results prove that the proposed contact-free system can be considered as an effective identity verification system for practical applications.     

手形特征点定位方法

针对特征点定位的准确性影响识别精度,提出手形特征点的定位方法。首先采用基于直线拟合的方法确定手指的指尖点,然后基于方向跟踪的方法定位手指指根点,最后细定位指根点。基于特征矢量匹配算法的识别结果表明,人工测量识别率为84.62%,自动定位的识别率为84.35%。该方法能准确提取手形特征,具有可行性。     

复杂环境中多信息融合的手势识别

针对复杂环境中的手势识别问题,提出了一种融合深度信息和红外信息的手势识别方法。首先利用Kinect摄像头的深度信息进行动态实时手势分割,然后融合红外图像复原手势区域。解决了实时手势分割和利用手势的空间分布特征进行手势识别时由于分割的手势区域有缺损或有人脸干扰时识别率低的问题。经实验验证,提出的方法不仅不受环境光线的影响,而且可以识别区分度较小的手势,对旋转、缩放、平移的手势识别也具有鲁棒性。对于区分度较大的手势,识别率高达100%。     

An interactive image clipping system using hand motion recognition

We present an efficient hand recognition algorithm for an interactive image clipping system, which is widely used for environments such as public facilities and security environments where personal capturing devices including mobile phones are not allowed. User-friendly interface and accurate image capturing function are required for an image clipping system. We build the system by combining Microsoft Kinect, HD webcam and projector. The Kinect and webcam are used to capture the motions of users׳ hand and project is to display the user-selected area from the capturing material. Hand recognition is composed of three steps: (i) the region occupied by users׳ hand is extracted from an image, (ii) the fingertips of the extracted hand region are analyzed using k-curvature algorithm, and (iii) the height of the fingertip is estimated using the depth image from Kinect. The height of the fingertip informs whether users׳ finger touched the surface of the target. The region captured by the fingertip is clipped from the image and stored as the target image. The excellence of our hand recognition algorithm is proved through a user test.     

基于Kinect的指尖检测与手势识别方法

针对基于视频的弯曲指尖点识别难、识别率不高的问题,提出一种基于深度信息、骨骼信息和彩色信息的手势识别方法。该方法首先利用Kinect相机的深度信息和骨骼信息初步快速判定手势在彩色图像中所在的区域,在该区域运用YCrCb肤色模型分割出手势区域;然后计算手势轮廓点到掌心点的距离并生成距离曲线,设定曲线波峰与波谷的比值参数来判定指尖点;最后结合弯曲指尖点特征和最大内轮廓面积特征识别出常用的12个手势。实验结果验证阶段邀请了6位实验者在相对稳定的光照环境条件下来验证提出的方法,每个手势被实验120次,12种手势的平均识别率达到了97.92%。实验结果表明,该方法能快速定位手势并准确地识别出常用的12种手势,且识别率较高。     

Personal identification based on finger-vein features

Finger-vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. The advantages of finger vein over traditional biometrics (e.g. face, fingerprint, and iris) lie in low-risk forgery, noninvasiveness, and noncontact. This paper here presents a new method of personal identification based on finger-vein recognition. First, a stable region representing finger-vein network is cropped from the image plane of an imaging sensor. A bank of Gabor filters is then used to exploit the finger-vein characteristics at different orientations and scales. Based on the filtered image, both local and global finger-vein features are extracted to construct a finger-vein code (FVCode). Finally, finger-vein recognition is implemented using the cosine similarity measure classifier, and a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results show that the proposed method exhibit an exciting performance in personal identification.     

Personal recognition based on an image of the palmar surface of the hand

This paper describes the design and development of a multimodal biometric personal recognition system based on features extracted from a set of 14 geometrical parameters of the hand, the palmprint, four digitprints, and four fingerprints. The features are extracted from a single high-resolution gray-scale image of the palmar surface of the hand using the linear discriminant analysis (LDA) appearance-based feature-extraction approach. The information contained in the extracted features is combined at the matching-score level. The resolutions of the palmprint, digitprint and fingerprint sub-images, the similarity/dissimilarity measures, the matching-score normalization technique, and the fusion rule at the matching-score level, which optimize the system performance, were determined experimentally. The biometric system, when using a system configuration with optimum parameters, showed an average equal error rate (EER) of 0.0005%, which makes it sufficiently accurate for use in high-security biometric systems.     

A biometric identification system based on eigenpalm and eigenfinger features

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).     

Tracking,recognition, and distance detection of hand gestures for a 3‐D interactive display

Abstract— This study proposes an interactive system for displays, the technologies of which consists of three main parts: hand‐gesture tracking, recognition, and depth measurement. The proposed interactive system can be applied to a general 3‐D display. In this interactive system, for hand‐gesture tracking, Haar‐like features are employed to detect a specific hand gesture to start tracking, while the mean‐shift algorithm and Kalman filter are adopted for fast tracking. First, for recognizing hand gestures, a principal component analysis (PCA) algorithm is used to localize colored areas of skin, and then hand gestures are identified by comparison with a prepared database. Second, a simple optical system is set up with an infrared laser source and a grid mask in order to project a proposed horizontal stripe pattern. Third, the projected patterns are deciphered to extract the depth information using the Hough‐transform algorithm. The system containing hand‐gesture localization, recognition, and associated depth detection (the distance between the display and the hand), was included in a prototype of an interactive display. Demonstration of rotation recognition of a finger‐pointing hand gesture was successful by using the algorithm of radar‐like scanning.     

A human-machine cooperative system for generating sign language animation using thermal image processing,a fuzzy algorithm,and simulated annealing

We propose a method for a sign language animation by skin region detection applied to an infrared thermal image. In a system incorporating the proposed method, a 3D CG model corresponding to a person’s characteristic posture while using sign language is generated automatically by pattern recognition of the thermal image, and then a person’s hand in the CG model is set. The hand part is made manually beforehand. If necessary, the model can be replaced manually by a more appropriate model corresponding to training key frames, and/or the same generated model can be refined manually. In our experiments, three hearing-impaired people, who were experienced in using sign language, recognized the Japanese sign language gestures of 70 words expressed as animations with 94.3% accuracy. We further improved the system by correcting the position and direction of the hand of the automatically generated model through the use of a fuzzy algorithm and simulated annealing.     

Personal Authentication Using Finger Knuckle Surface

This paper investigates a new approach for personal authentication using fingerback surface imaging. The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. The finger geometry features can be simultaneously acquired from the same image at the same time and integrated to further improve the user-identification accuracy of such a system. The fingerback surface images from each user are normalized to minimize the scale, translation, and rotational variations in the knuckle images. This paper details the development of such an approach using peg-free imaging. The experimental results from the proposed approach are promising and confirm the usefulness of such an approach for personal authentication.      

Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification

We propose a method of personal identification based on finger-vein patterns. An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions. Experimental results show that it achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.Received: 27 October 2003, Accepted: 25 February 2004, Published online: 21 July 2004     

Finger vein recognition with manifold learning

Finger vein is a promising biometric pattern for personal identification in terms of its security and convenience. However, so residual information, such as shade produced by various thicknesses of the finger muscles, bones, and tissue networks surrounding the vein, are also captured in the infrared images of finger vein. Meanwhile, the pose variation of the finger may also cause failure to recognition. In this paper, for the first time, we address this problem by unifying manifold learning and point manifold distance concept. The experiments based on the TED-FV database demonstrate that the proposed algorithmic framework is robust and effective.     

Mutual adaptation among man and machine by using f-MRI analysis

A prosthetic device for disabled people requires new and reliable robotics technology. This paper describes the interesting reaction of our brain to an adaptable prosthetic system. The adaptable prosthetic system is composed of an EMG signal controlled robot hand with an EMG pattern recognition learning function for Transradial (below elbow) prostheses. The mutual adaptation between the system and the human body is analyzed using functional magnetic resonance imaging (f-MRI) in order to clarify the plasticity of the motor and sensory cortex area according to the changes in the prosthetic system. The developed prosthetic hand has 13 DOF: three motors on the thumb, two motors for each finger, and two motors for the wrist. The tactile feedback is applied by using surface electrical stimulus. The f-MRI data shows the process of replacement from a phantom limb image to the prosthetic hand image.     

基于指节纹的个人身份自动识别

人体的指节纹具有稳定性且对于不同的人具有不同的位置和结构特征, 可作为身份识别的依据. 本文提出了一种基于指节纹的身份识别新方法: 对采集的手掌图像首先通过预处理分割出各手指并旋转至水平位置; 然后用Sobel算子求其水平梯度, 对梯度图二值化后经垂直投影得到一维向量; 对此向量应用小波去噪, 生成手指指节纹特征向量; 通过用余弦函数计算指节纹特征向量之间相似度实现最后的匹配. 本文用该方法对来自190个手掌的1900个样本进行了测试, 取得了0.67%的等误率, 单次匹配时间低于2ms. 实验结果表明该方法具有较高的识别精度, 而且识别速度快, 适合在大规模手掌库中实现手掌筛选.     

基于GPRS传输的新型输电线路红外热像检测系统

针对目前野外输电线路在线温度实时检测的薄弱环节,提出了一种基于GPRS传输的的新型输电线路红外热像检测系统设计方案,克服了现有红外热像探测技术只能进行有线检测,无法实现长距离无线检测的问题,实现了对野外输电线路的实时红外检测。介绍了该检测系统的总体结构及其工作原理,详细阐述了数据采集终端各子系统和监控主机的软硬件设计。现场运行试验表明,该系统较好地实现了对野外输电线路的实时或定时的红外热图像故障检测。     

Online finger-knuckle-print verification for personal authentication

Biometric based personal authentication is an effective method for automatically recognizing, with a high confidence, a person's identity. By observing that the texture pattern produced by bending the finger knuckle is highly distinctive, in this paper we present a new biometric authentication system using finger-knuckle-print (FKP) imaging. A specific data acquisition device is constructed to capture the FKP images, and then an efficient FKP recognition algorithm is presented to process the acquired data in real time. The local convex direction map of the FKP image is extracted based on which a local coordinate system is established to align the images and a region of interest is cropped for feature extraction. For matching two FKPs, a feature extraction scheme, which combines orientation and magnitude information extracted by Gabor filtering is proposed. An FKP database, which consists of 7920 images from 660 different fingers, is established to verify the efficacy of the proposed system and promising results are obtained. Compared with the other existing finger-back surface based biometric systems, the proposed FKP system achieves much higher recognition rate and it works in real time. It provides a practical solution to finger-back surface based biometric systems and has great potentials for commercial applications.     

树状小波局部对比度与图像融合及性能评估

针对红外热成像中目标识别和跟踪的特点,提出了一种基于树状小波变换的局部对比度融合算法,首先采用多尺度树状小波变换的方法对已配准的源图像在相应的能量准则下进行分解,克服了小波变换的移变性;对于分解后的低频子图像采用加权平均的融合规则,对于高频子图像采用局部对比度量测的融合规则,融合图像既保持了源图像的细节信息,又滤去了红外成像中的各类噪声。采用交叉分辨力评价算子量测红外图像中目标与背景的衬比度;通过仿真及基于客观的图像融合评价标准,分别从信息熵、标准差、平均梯度和交叉熵四个参数对图像融合效果进行评估,证明了对于双波段红外辐射图像的融合,提出的融合算法优于小波融合算法、形态学区域分割融合算法。算法尤其适用于红外热成像系统的目标识别和跟踪。