Fusion of palm-phalanges print with palmprint and dorsal hand vein

To ensure the high performance of a biometric system, various unimodal systems are combined to evade their constraints to form a multimodal biometric system. Here, a multimodal personal authentication system using palmprint, dorsal hand vein pattern and a novel biometric modality “palm-phalanges print” is presented. Firstly, we have collected a new anterior hand database of 50 individuals with 500 images at the institute referred to as NSIT Palmprint Database 1.0 by using NSIT palmprint device. Then from these anterior hand images, database for palmprint and palm-phalanges is created. In this biometric system, the individuals do not have to undergo the distress of using two different sensors since the palmprint and palm-phalanges print features can be captured from the same image, using NSIT palmprint device, at the same time. For dorsal hand vein, Bosphorus Hand Vein Database is used because of the stability and uniqueness of hand vein patterns. We propose fusion of three different biometric modalities which includes palmprint (PP), palm-phalanges print (PPP) and dorsal hand vein (DHV) and perform score level fusion of PP-PPP, PP-DHV, PPP-DHV and PP-PPP-DHV strategies. Lastly, we use K-nearest neighbor, support vector machine and random forest to validate the matching stage. The results proved the validity of our proposed modality and show that multimodal fusion has an edge over unimodal fusion.     

Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person's identity with high confidence. It is well recognized that in biometric systems feature extraction and representation are key considerations. Among various feature extraction and representation schemes, coding-based methods are most attractive because they have the merits of high accuracy, robustness, compactness and high matching speed, and thus they have been adopted in many different kinds of biometric systems, such as iris, palmprint, and finger-knuckle-print based ones. However, how to devise a good coding scheme is still an open issue. Recent studies in image processing and applied mathematics have shown that local image features can be well extracted with Riesz transforms in a unified framework. Thus, in this paper we propose to utilize Riesz transforms to encode the local patterns of biometric images. Specifically, two Riesz transforms based coding schemes, namely RCode1 and RCode2, are proposed. They both use 3-bits to represent each code and employ the normalized Hamming distance for matching. RCode1 and RCode2 are thoroughly evaluated and compared with the other 3-bit coding methods on a palmprint database and a finger-knuckle-print database. Experiments show that the proposed methods, especially RCode2, could achieve quite similar verification accuracies with the state-of-the-art method (CompCode) while they need much less time at the feature extraction stage, which renders them better candidates for time critical applications.     

Empirical study of light source selection for palmprint recognition

Most of the current palmprint recognition systems use an active light to acquire images, and the light source is a key component in the system. Although white light is the most widely used light source, little work has been done on investigating whether it is the best illumination for palmprint recognition. This study analyzes the palmprint recognition performance under seven different illuminations, including the white light. The experimental results on a large database show that white light is not the optimal illumination, while yellow or magenta light could achieve higher palmprint recognition accuracy than the white light.     

Score normalization in multimodal biometric systems

Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. Although information fusion in a multimodal system can be performed at various levels, integration at the matching score level is the most common approach due to the ease in accessing and combining the scores generated by different matchers. Since the matching scores output by the various modalities are heterogeneous, score normalization is needed to transform these scores into a common domain, prior to combining them. In this paper, we have studied the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user. Experiments conducted on a database of 100 users indicate that the application of min–max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods. However, experiments also reveal that the min–max and z-score normalization techniques are sensitive to outliers in the data, highlighting the need for a robust and efficient normalization procedure like the tanh normalization. It was also observed that multimodal systems utilizing user-specific weights perform better compared to systems that assign the same set of weights to the multiple biometric traits of all users.     

基于Gabor小波核心算法的指纹图像预处理

本文介绍了一种基于小波技术的指纹图像增强算法——Gabor滤波。详细的阐述了Gabor滤波器的构造及其在指纹图像增强中的应用，并从原理上分析了应用中遇到的问题，提出了改进的方法。     

Discriminative multimodal biometric authentication based on quality measures

A novel score-level fusion strategy based on quality measures for multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted every time an authentication claim is performed based on the estimated quality of the sensed biometric signals at this time. Experimental results combining written signatures and quality-labelled fingerprints are reported. The proposed scheme is shown to outperform significantly the fusion approach without considering quality signals. In particular, a relative improvement of approximately 20% is obtained on the publicly available MCYT bimodal database.     

Gait recognition based on joint distribution of motion angles

Gait as a biometric trait has the ability to be recognized in remote monitoring. In this article, a method based on joint distribution of motion angles is proposed for gait recognition. The new feature of the motion angles of lower limbs is defined and extracted from either 2D video database or 3D motion capture database, and the corresponding angles of right leg and left leg are joined together to work out the joint distribution spectrums. Based on the joint distribution of these angles, we build a feature histogram individually. In the stage of distance measurement, three types of distance vector are defined and utilized to measure the similarity between the histograms, and then a classifier is built to implement the classification. Experiments has been carried out both on CASIA Gait Database and CMU motion capture database, which show that our method can achieve a good recognition performance.     

基于手轮廓的深度图像手势识别方法

提出了一种新的手势识别方法,该方法从深度图像中提取手形轮廓,通过计算手形轮廓与轮廓形心点的距离,使用离散傅里叶变换获得手势的表观特征,引入径向基核的支持向量机识别手势。建立了一个常见的10种手势的数据集,测试获得了97.9%的识别率。     

图像识别的机器手抓握滑移检测系统开发

为实现机器手抓握物体时不发生脱落,首先应检测其与被抓握物体接触面上的滑移信号.提出一种基于图像识别的机器手抓握滑移检测方法,采用中心区域匹配思想的归一化互相关算法(NCC)匹配由视觉传感器实时采集到的被抓握物体表面图像,得到被抓握物体在采集图像期间的滑移情况.实验结果表明:此系统可以准确检测被抓握物体是否发生滑移及滑移的方向和大小,具有高准确度、高灵敏度等优点.     

基于图像识别的袋装粮数量识别研究*

设计了一种基于平面图像识别的袋装粮数量智能识别方法。对粮库现场采集的图像样本,运用数字图像处理技术对图像进行降噪和对象体特征突显;然后运用区域增长法对图像中每一闭合像素区域进行聚类分割;最后根据相应几何特征值判定粮袋身份并通过其椭圆度的范围,以实现粮袋所属面的判定,达到对粮堆重量的自动识别。     

Person recognition by hand shape based on skeleton of hand image

A new approach to the biometrical identification of a person based on the analysis of his hand shape is suggested in this paper. The shape is compared for reference and test hands. The reference hand is a preset model of the hand, while the test hand is the binary image obtained using a web-camera. In comparing the hans, the model’s adjustment to the test image is carried out for the best coincidence of their silhouettes. In contrast to the existing algorithms, the propsed method allows one to compare hands with partly occluded fingers, which are often observed under real conditions. The paper also describes a prototype of the system which allows real-time person recognition.     

Applications of thermal image and extension theory to biometric personal recognition

In this paper, a new scheme is proposed to design a biometric personal recognition system. First, this paper used the thermal image of the hand by using infrared camera to build the sensor module of the recognition system; the extraction features include the length of palmar midpoint to each finger, palmar profile, finger length and finger width. The thermal image presented in this paper was detects infrared energy and converts it into an electronic signal. Then a new recognition method based on the extension is proposed to perform the core of the personal recognition system. The experimental results confirmed that proposed recognition system has a very high recognition rates, therefore, this paper verification using thermal image of the hand to identity recognition was feasible.     

基于不变矩的步态识别

提出了一种利用不变矩进行步态识别的方法。该方法把二维人体空间轮廓信号变换为一维不变矩信号,把人体的步态序列变换为不变矩矢量,对不变矩矢量进行规格化,然后根据规格化不变矩矢量进行步态识别。实验中,本文的方法取得很好的效果。     

基于加权分块稀疏表示的光照鲁棒性人脸识别

针对光照变化对人脸识别的效果带来严重影响,提出一种对人脸识别的光照变化具有鲁棒性的方法,即基于加权分块稀疏表示的人脸识别方法。该方法首先对人脸图像进行离散余弦变换（DCT）,通过去除 DCT 系数的低频部分来移除光照变化分量。通过反离散余弦变换得到光照归一化后的人脸图像,将人脸图像分块,独立地对每个子块作基于稀疏表示的分类,并对每个子块的分类结果进行加权投票得出测试人脸图像的类别。在 Yale B、extended-Yale B、CMU-PIE 和 FERET 人脸库上进行实验,实验结果表明该方法适用于光照鲁棒的人脸识别。     

基于游程特征的文本图像识别方法

提出了一种基于游程特征的中英文本图像识别方法,用游程统计特征提供的图像信息作为图像模式识别的描述特征,在此基础上利用神经网络来训练分类器。实验结果表明,该方法的识别精度较高,具有一定的容错能力。     

基于神经网络集成的汽车牌照识别

对基于神经网络集成的汽车牌照识别的原理和方法进行了研究,并着重分析了现有技术的积极因素和潜在问题,提出了一种基于神经网络集成进行车牌文字识别的方法.在特征提取时采用了多种特征提取的方法,对提取的每种特征构建一个BP神经网络分别进行训练.最终待识别的字符将被神经网络集成进行识别.实践证明,利用该方法比单个神经网络识别有更高的识别率,具有较高的使用价值.     

Personal driving diary: Automated recognition of driving events from first-person videos

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person’s daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.     

航天运载器端面特征的新型图像特征识别方法

基于航天运载器端面的固有特征,结合现有航天图像测量系统的硬件平台,提出一种端面图像特征提取与识别方法,获取视场内关键目标端面特征信息,对后续图像物理量参数优化解算起到指导性作用.以运载器端面椭圆特征为基础,利用Canny算子检测出图像中端面椭圆边缘特征,将特征边缘拆分为若干弧段,通过弧段拟合、弧段合并、椭圆提取等方法,实现端面特征的准确提取.仿真试验结果表明:该图像特征识别方法实时性好、精度高,可显著提高运载器图像测量能力.     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.