Wireless Network Security Based on the Image Address Masking (IAM) Mechanism

A multimodal biometric system is applied to recognize individuals as authentication, identification and verification for claimed identity. Multimodal biometrics increases the security level accuracy, spoof of attacks, noise in collected data, intra-class variations, inter-class variations, non universality etc. In this paper a multi modal biometric algorithm is designed by integrating iris, palm print, face and signature based on encoded discrete wavelet transform for image analysis and authentication. Multi level wavelet based fusion approach is applied, integrated and encoded into single composite image for matching decision. It reduces the memory size, increases the recognition accuracy and ERR using multimodal biometric approach when compared to individual biometric traits. The complexity of fusion and the reconstruction algorithm is suitable for many real time applications.     

人脸与虹膜特征层融合模型的研究

多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图像与虹膜图像分别进行压缩降维处理,得到相应的初始特征矩阵.然后将人脸与虹膜的初始特征矩阵进行组合,获得组合特征矩阵.同时,利用2DFLD算法对组合特征矩阵进行融合,获得了人脸与虹膜的融合特征.最后运用最小距离分类器进行识别.基于ORL(Olivetti Research Laboratory)人脸数据库和CASIA(Chinese Academy of Sciences,Institute of Automation)虹膜数据库的实验结果表明,该模型实现了特征层融合,不仅克服了"小样本"效应,而且有效提高了身份识别的正确识别率,为多生物特征身份识别提供了一种有效模型.     

基于视频的人脸和步态自适应融合身份识别

多生物特征融合的主要目的是利用其互补性来提高系统的识别性能.主要针对行走视频中人脸和步态两个生物特征融合的识别方法进行研究,对多个角度视频下的人脸和步态提出了基于决策层的自适应加权融合方法,实验结果证明该方法的融合识别结果比单一生物识别方法以及最大法则、加权法则等融合算法具有更高的识别率.     

Appearance-based periocular features in the context of face and non-ideal iris recognition

Developing newer approaches to deal with non-ideal scenarios in face and iris biometrics has been a key focus of research in recent years. The same reason motivates the study of the periocular biometrics as its use has a potential of significantly impacting the iris- and face-based recognition. In this paper, we explore the utility of the various appearance features extracted from the periocular region from different perspectives: (i) as an independent biometric modality for human identification, (ii) as a tool that can aid iris recognition in non-ideal situations in the near infra-red (NIR) spectrum, and (iii) as a possible partial face recognition technique in the visible spectrum. We employ a local appearance-based feature representation, where the periocular image is divided into spatially salient patches, appearance features are computed for each patch locally, and the local features are combined to describe the entire image. The images are matched by computing the distance between the corresponding feature representations using various distance metrics. The evaluation of the periocular region-based recognition and comparison to face recognition is performed in the visible spectrum using the FRGC face dataset. For fusion of the periocular and iris modality, we use the MBGC NIR face videos. We demonstrate that in certain non-ideal conditions encountered in our experiments, the periocular biometrics is superior to iris in the NIR spectrum. Furthermore, we also demonstrate that recognition performance of the periocular region images is comparable to that of face in the visible spectrum.     

Symmetric ear and profile face fusion for identical twins and non-twins recognition

Humans have bilateral body symmetry such that the left and right sides are mirror images of each other. This study tries to measure the performance on human recognition where the stored templates in the database are acquired from one side of a biometric trait such as left profile face, while the tested samples correspond to the other side of the same trait after applying a horizontal flip. Two different biometric traits are used in this study, namely profile face and ear biometrics. The experiments are conducted using the feature extraction methods namely Principal Component Analysis, Scale-Invariant Feature Transform, Local Binary Patterns, Local Phase Quantization and Binarized Statistical Image Features. Several experiments are performed on identical twins and non-twins individuals using ND-Twins-2009-2010 and UBEAR databases. Furthermore, the symmetry of profile face and ear is used to propose a hybrid approach of human recognition system that involves feature-level and score-level fusion of both traits. The proposed method is superior to all the unimodal and multimodal biometric methods that are implemented in this study for human recognition in the case of symmetry.     

Complex common vector for multimodal biometric recognition

A novel multimodal biometric recognition algorithm based on a complex common vector (CCV) is proposed. The CCV generalises the common vector method for the complex field to perform feature fusion and classification. Theoretical analysis proves that the CCV could produce a unique common vector for every fusion feature in a given class. The iris and the face are used as two distinct biometric modals to test the algorithm. Experimental results show that the proposed algorithm achieves much better performance than other conventional multimodal biometric algorithms.     

Concealment of iris features based on artificial noises

Although iris recognition verification is considered to be the safest method of biometric verification, studies have shown that iris features may be illegally used. To protect iris features and further improve the security of iris recognition and verification, this study applies the Gaussian and Laplacian mechanisms and to hide iris features by differentiating privacy. The efficiency of the algorithm and evaluation of the image quality by the image hashing algorithm are selected as indicators to evaluate these mechanisms. The experimental results indicate that the security of an iris image can be significantly improved using differential privacy protection.     

Fusion of face and iris biometrics using local and global feature extraction methods

Fusion of multiple biometrics combines the strengths of unimodal biometrics to achieve improved recognition accuracy. In this study, face and iris biometrics are used to obtain a robust recognition system by using several feature extractors, score normalization and fusion techniques. Global and local feature extractors are used to extract face and iris features separately, and then, the fusion of these modalities is performed on different subsets of face and iris image databases of ORL, FERET, CASIA and UBIRIS. The proposed method uses Local Binary Patterns local feature extractor and subspace Linear Discriminant Analysis global feature extractor on face and iris images, respectively. Face and iris scores are normalized using tanh normalization, and then, Weighted Sum Rule is applied for the fusion of these two modalities. Improved recognition accuracies are achieved compared to the individual systems and multimodal systems using other local or global feature extractors for both modalities.     

基于二维Fisher线性判别的人脸耳组合识别

针对人脸易受到年龄、表情等影响,提出了脸和耳相结合的组合识别方法。利用二维Fisher线性判别(2DFLD)方法分别进行了脸、耳图像层和特征层的组合识别。在北京科技大学人耳库和ORL人脸库上进行实验,结果表明,图像层组合和特征层组合的识别率分别为97.5%、95.0%,分别比人脸识别提高了12.5%和10.0%,比人耳识别提高了5.0%和2.5%;与同样应用于组合识别的主成分分析(PCA)、二维PCA(2DPCA)比较,也取得了较好识别效果。这说明,多生物特征组合识别是一种有效的识别方法。     

Speeded up robust features for efficient iris recognition

Iris recognition system is one of the biometric systems in which the development is growing rapidly. In this paper, speeded up robust features (SURFs) are used for detecting and describing iris keypoints. For feature matching, simple fusion rules are applied at different levels. Contrast-limited adaptive histogram equalization (CLAHE) is applied on the normalized image and is compared with histogram equalization (HE) and adaptive histogram equalization (AHE). The aim is to find the best enhancement technique with SURF and to verify the necessity of iris image enhancement. The recognition accuracy in each case is calculated. Experimental results demonstrate that CLAHE is a crucial enhancement step for SURF-based iris recognition. More keypoints can be extracted with enhancement using CLAHE compared to HE and AHE. This alleviates the problem of feature loss and increases the recognition accuracy. The accuracies of recognition using left and right iris images are 99 and 99.5 %, respectively. Fusion of local distances and choosing suitable fusion rules affect the recognition accuracy, noticeably. The proposed SURF-based algorithm is compared with scale-invariant feature transform, histogram of oriented gradients, maximally stable extremal regions and DAISY. Results show that the proposed algorithm is robust to different image variations and gives the highest recognition accuracy.     

基于总错误率和特征关联的自适应融合多模态生物特征识别

针对单生物特征识别准确率和鲁棒性差的问题, 提出了一种基于总错误率(TER)和特征关联自适应融合多模态生物特 征识别方法。首先将TER作为判别特征引入到多模态识别,以代替传统的匹配分 数;其次在不确定度量理论的基 础上,考虑人脸特征和语音特征之间的时空关联性,提出了一种基于特征关联的多特征 自适应融合策略,利用特征关联 系数自适应调节不同识别特征对识别结果的贡献。仿真实验表明,与几种代表性的融合算法 相比,本文所 提出的融合模式可以有效提高多生物特征识别系统的准确性和鲁棒性。     

Special issue on microscopic image processing

The applications of biometric technology for automated personal identification become ubiquitous. Iris recognition is well known for high accuracy and reliability among the biometric traits. This paper presents an efficient noise-removing approach for non-cooperative iris recognition systems. Proposed method removed the noise factors including eyelids, eyelashes, reflections, out of framework, pupil and sclera. The novelty is to detect eyelashes and reflections through finding appropriate thresholds using a procedure called statistical decision making. The eyelids are detected using parabolic Hough transform in normalized iris image to increase computational speed. In addition, a coarse-to-fine strategy for accurate and fast iris localization is proposed. The Gabor-wavelet and a novel encoding strategy proposed in our previous work are also used here to generate the iris codes. We elaborate the principle of mask code generation to assign noisy bits in an iris code to exclude them in matching step. Experimental results on CASIA-IrisV3-Interval database show superiority of the proposed scheme among other state-of-the-art methods available in the literature.     

Compression-Robust and Fuzzy-Based Feature-Fusion Model for Optimizing the Iris Recognition

Iris Recognition is gaining popularity in various online and offline authentication and multi-model biometric systems. The non-altering and non-obscuring nature of Iris have increased its reliability in authentication systems. The iris images captured in an uncontrolled environment and situation is the challenging issue of the iris recognition. In this paper, a compression robust and KPCA-Gabor fused model is presented to recognize the iris image accurately under these complexities. The illumination and noise robustness is included in this pre-processing stage for gaining the robustness and reliability against complex capturing. The effective compression features are generated as a phase pre-treatment vector using the Logarithmic quantization method. (Kernel Principal Component Analysis) KPCA and Gabor filters are applied to the rectified image for generating the textural features. The compression is also applied to Gabor and KPCA filtered images. The fuzzy adaptive content level fusion is applied to the compression image, KPCA-Compression, and Gabor-Compression iris-image. (K-Nearest Neighbors) KNN based mapping is used to this composite-fused and reduced feature set to recognize the individual. The proposed compression and fusion-feature based model is applied to CASIA-Iris, UBIRIS, and IITD datasets. The comparative evaluations against earlier approaches identify that the proposed model has improved the recognition accuracy and the reduction in error-rate is also achieved.

     

一种新颖的基于小波变换的虹膜识别算法

虹膜识别是一种新兴的生物识别技术,研究了虹膜图像的快速定位、增强等预处理方法,并提出了一种新颖的基于二维小波变换的虹膜识别算法,它的基本思想是：对预处理得到的虹膜图像进行四层小波分解,对得到的低频和高频系数抽取115维特征矢量,最后用汉明距离进行图像匹配判决合法性。实验证明,该算法运算速度较快,在图像模糊、噪声干扰等不利条件下仍具有良好的稳健性。     

基于最大Gabor相似度的多光谱人脸识别系统

以精准识别不同特征维数以及噪声情况下的多光谱人脸为目标,设计基于最大Gabor相似度的多光谱人脸识别系统.原始可见光光谱图像以及热红外光谱图像分别输入系统的可见光光谱图像处理模块以及热红外光谱图像处理模块,图像处理模块分别提取原始可见光光谱图像以及热红外光谱图像特征并建立特征集,多光谱图像融合模块基于特征集内特征利用双...     

Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments

Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems based on the algorithms developed by John Daugman have been available since 1995 and have been used in a variety of practical applications. However, all currently available systems impose substantial constraints on subject position and motion during the recognition process. These constraints are largely driven by the image acquisition process, rather than the particular pattern-matching algorithm used for the recognition process. In this paper we present results of our efforts to substantially reduce constraints on position and motion by means of a new image acquisition system based on high-resolution cameras, video synchronized strobed illumination, and specularity based image segmentation. We discuss the design tradeoffs we made in developing the system and the performance we have been able to achieve when the image acquisition system is combined with a standard iris recognition algorithm. The Iris on the Move (IOM) system is the first system to enable capture of iris images of sufficient quality for iris recognition while the subject is moving at a normal walking pace through a minimally confining portal     

Score bi-Gaussian equalisation for multimodal person verification

Multimodal biometric fusion at score level can be performed by means of combinatory or classificatory techniques. In the first case, it is straightforward that the normalisation of the scores is a very important issue for the success of the fusion process. In the classificatory approach as, for instance, in support vector machine (SVM)- based systems, only simple normalisation methods are usually applied. In this work, histogram equalisation of biometric score distribution is successfully applied in a multimodal person verification system composed by prosodic, speech spectrum and face information. Furthermore, a new bi-Gaussian equalisation (BGEQ) is introduced, which takes into account the separate statistics of the genuine and impostor scores by using as a reference a sum of two Gaussian functions, whose standard deviations model the overlap between the genuine and impostor lobes of the original distributions. Multimodal verification experiments are shown, where prosodic and speech spectrum scores are provided by speech experts using the Switchboard-I database, and face scores are obtained by a face recognition expert using XM2VTS database. BGEQ in combination with an SVM fusion system with a polynomial kernel has obtained the best results and has outperformed in more than a 21.29% the results obtained by min?max normalisation.     

Nonintrusive Iris Image Extraction for Iris Recognition-Based Biometric Identification

This paper proposes a new approach to obtain iris images without requiring the person to look directly into the camera. Most biometric identification methods using iris recognition assume that the eye image of the person is available, and are only concerned with the extraction of the iris from the eye image. These methods require the person to look directly into the camera, which is a rather uncomfortable process. In this paper, a robust approach is proposed to initially locate eye regions within facial images taken from a distance. Then the iris image is extracted from the detected regions. Hence, a nonintrusive and more comfortable iris imaging approach has been implemented.