H.264熵解码器的研究与实现

在H.264/AVC标准中,基于上下文的自适应可变长编码(CAVLC)解码算法的复杂度较高。为此,提出一种基于熵解码算法的新型熵解码器,在对视频压缩码流实现熵解码的过程中,引入并行处理方式,并改进二叉树法。通过采用QuartusⅡ7.2版环境波形仿真和FPGA硬件实现方法进行实验,结果表明该熵解码器在硬件资源节省和解码速度方面具有较好的性能。     

基于改进的局部敏感判别分析的步态识别方法

相对于人脸和指纹等广泛使用的生物特征识别手段而言,步态识别是一种相对新的非接触式的身份识别方法。提出了一种基于改进的局部敏感判别分析的步态识别方法。在真实的步态数据库上的实验结果表明,提出的步态识别方法是有效可行的。     

Scalable watermark extraction for real-time authentication of JPEG 2000 images

This paper proposes a novel scalable authentication scheme that utilizes the progressive enhancement functionality in JPEG 2000 scalable image coding. The proposed method first models the wavelet-based quality scalable coding to identify the effect of the quantization and de-quantization on wavelet coefficient magnitudes and the data embedded within such coefficients as a watermark. A relationship is then established between the watermark extraction rule and the embedding rule, using the magnitudes of the reconstructed and original coefficients. It ranks the wavelet coefficients according to their ability to retain the embedded watermark data intact under various quantization levels corresponding to quality enhancements. Then watermark data is embedded into wavelet coefficients according to their rank followed by JPEG 2000 embedded coding. At the decoder as more and more quality and resolution layers are decoded the authentication metric is improved, thus resulting in gradually increasing complexity of the authentication process according to the number of quality and resolution enhancements. The low complexity authentication is available at low quality low resolution decoding, enabling real-time authentication for resource constrained applications without affecting the authentication metric. Compared to the existing methods, the proposed method results in highly robust scalable authentication of JPEG 2000 coded images.     

基于生物识别的网络身份认证新方案

文章分析了现今基于生物识别技术的网络认证没被广泛应用的原因：生物特征的提取一般需要特殊的专用设备、指纹的利用比较泛滥、生物特征遗失后挂失比较困难等。文章同时对比了几种比较主流的生物特征识别技术,分析了它们各自的实用性、便捷性以及安全性,指出生物3D打印技术在未来对生物识别技术带来冲击的可能。文章提出了一种基于动态人脸识别的网络认证方案,该方案利用人脸作为网络认证的基础,通过跟踪实时人脸活动来实现实时人脸图像的采集,预防了照片攻击和视频攻击,提高了认证的可靠性和安全性。文章最后通过分析该方案的可实现性、可叠加性和安全性,并从成本等方面考虑,得出该方案性能较优的结论,同时对生物识别技术应用于网络认证进行了展望。由于生物特征具有唯一性和不可重置的特点,所以生物特征保护需要引起更高的关注,也需要更多学者做相关的研究,更好地利用生物特征。     

Human Gait Recognition Based on Kernel PCA Using Projections

This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis （PCA）, to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.     

Gait verification using knee acceleration signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively.     

基于智能手机运动传感器的步态特征身份识别方法

利用行为特征进行身份验证是生物识别的前沿技术。为优化基于步态特征的身份识别研究中对数据的处理并改进识别的方式，提出利用智能手机运动传感器数据提取步态特征用于身份识别的方法。首先，应用空间转换算法解决传感器坐标系漂移问题，使数据可以完整准确地刻画行为特征；然后，利用支持向量机（SVM）算法对用户切换所导致的步态特征变化进行分类识别。实验结果表明，经过欧拉角法处理后，所提方法识别准确率达到95.5%，在有效识别用户变换的同时降低了空间开销和实现难度。     

基于帧差能量图行质量向量的步态识别算法

为了有效地捕捉步态的连续性动态信息,快速进行身份认证和识别,提出一种以帧差能量图（FDEI）的行质量向量作为步态特征的步态识别方法。该算法通过目标检测、二值化、形态学处理、连通性分析等预处理后得到步态轮廓图像,并利用其序列的宽度进行准周期性分析,再用连续隐马尔可夫模型(CHMM)对所提取的步态帧差能量图行质量向量进行模型参数训练和识别。在CASIA数据库上进行了仿真实验,结果表明该算法具有特征提取简单、特征维数低、识别速度快和识别率高的优点,可以满足实时识别的需要。     

Tamper detection and self-recovery of biometric images using salient region-based authentication watermarking scheme

Biometric images can be split into regions of salient (ROI) and regions of background (ROB) based on salient region detection. During the process of watermark embedding, salient regions which contain rich-information are less affected by the watermark, therefore can be embedded into more watermarks, and regions of background (ROB) are susceptible to the effect of watermarks, so that they should be embedded lighter or even none in order to preserve the recognition quality of biometric images. In this paper, a novel scheme for tamper detection and self-recovery of biometric images using salient region-based authentication watermarking is proposed. Firstly, we propose a novel multi-level authentication watermarking scheme, which is used to verify the integrity of biometric images. Secondly, biometric data of these biometric images which is used as information watermarks is embedded into themselves. As a result, verification systems can recover the damaged data of original biometric images with hidden information based on tampering detection result. Experimental results and theoretic analysis show that our proposed scheme can detect tampered regions, and recover biometric data while keeping the recognition quality.     

A fast feature selection technique in multi modal biometrics using cloud framework

Biometrics refers to the process that uses biological or physiological traits to identify individuals. The progress seen in technology and security has a vital role to play in Biometric recognition which is a reliable technique to validate individuals and their identity. The biometric identification is generally based on either their physical traits or their behavioural traits. The multimodal biometrics makes use of either two or more of the modalities to improve recognition. There are some popular modalities of biometrics that are palm print, finger vein, iris, face or fingerprint recognition. Another important challenge found with multimodal biometric features is the fusion, which could result in a large set of feature vectors. Most biometric systems currently use a single model for user authentication. In this existing work, a modified method of heuristics that is efficiently used to identify an optimal feature set that is based on a wrapper-based feature selection technique. The proposed method of feature selection uses the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are used to feature extraction and classification process utilizes the integration of face, and finger print texture patterns. The set of training images is converted to grayscale. The crossover operator is applied to generate multiple samples for each number of images. The wok proposed here is pre-planned for each weight of each biometric modality, which ensures that even if a biometric modality does not exist at the time of verification, a person can be certified to provide calculated weights the threshold value. The proposed method is demonstrated better result for fast feature selection in bio metric image authentication and also gives high effectiveness security.     

基于GVF Snake模型的步态识别轮廓提取

步态识别作为一种新的生物识别技术,通过人走路的姿势实现对个人身份的识别和认证。步态特征提取是步态识别的关键步骤。采用背景消减法与对称差分法相结合对运动人体分割,采用改进的GVF Snake模型对人体运动步态轮廓进行边缘提取。实验结果表明该方法能准确高效地提取边缘特征作为步态识别的特征。     

Robust palmprint verification using 2D and 3D features

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features. The objective of our work is to improve accuracy and robustness of existing palmprint authentication systems using 3D palmprint features. The proposed multilevel framework for personal authentication efficiently utilizes the robustness (against spoof attacks) of the 3D features and the high discriminating power of the 2D features. The developed system uses an active stereo technique, structured light, to simultaneously capture 3D image or range data and a registered intensity image of the palm. The surface curvature feature based method is investigated for 3D palmprint feature extraction while Gabor feature based competitive coding scheme is used for 2D representation. We comparatively analyze these representations for their individual performance and attempt to achieve performance improvement using the proposed multilevel matcher that utilizes fixed score level combination scheme to integrate information. Our experiments on a database of 108 subjects achieved significant improvement in performance with the integration of 3D features as compared to the case when 2D palmprint features alone are employed. We also present experimental results to demonstrate that the proposed biometric system is extremely difficult to circumvent, as compared to the currently proposed palmprint authentication approaches in the literature.     

Attention-based neural joint source-channel coding of text for point to point and broadcast channel

In this work, we consider the transmissions of structured data such as text over a noisy channel and correlated texts over a broadcast channel. As the separate source-channel coding principle no longer holds in such scenarios, we propose a joint source-channel coding scheme which is based on deep learning architecture. In order to enhance the convergence speed, we adopt the bidirectional gated recurrent unit at the encoder. For the decoder, to improve the recovery quality, we propose the following two types of strategies: (1) After a unidirectional neural network based decoder is used, a generative adversarial network is applied to train the whole joint source-channel coding framework and pointwise mutual information is added to the objective function of beam search process; (2) Rather than using a unidirectional neural network-based decoder, we develop a bidirectional neural network based and bidirectional attention mechanism integrated decoder to utilize past and future information. Experiments under different types of channels show that our schemes are superior to the existing deep learning joint source-channel coding method and in the case of low bit budget, long sentence length and small channel signal to noise ratio, our models are significantly superior to those of separate source-channel coding. In addition, we extend the proposed unidirectional and bidirectional decoders to the broadcast channel. Additionally, to improve the performance of unidirectional decoding, we utilize not only the correlation between adjacent words in the same text but also the correlation between words in different languages with the same meaning in the beam search process.

     

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.     

View-invariant gait recognition based on kinect skeleton feature

Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance.     

加权CCA多信息融合的步态表征方法

为了解决步态识别中步态表征不完备的问题,提出了一种新的步态表征方法。该方法是在步态流图的基础上,将能够表征时序信息的步宽特征编码到颜色空间,得到三通道的彩色类能量图,采用典型相关分析将多通道信息融合成单通道,同时去除了特征间的冗余信息,得到了更丰富的有益识别的步态特征。实验结果表明,提出的新方法能够有效提取步态特征,步态识别率得到显著提高。     

Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting these images to YIQ color space, which is helpful in normalizing the brightness levels of the image hence mainly (Y component’s) luminance. Then, it attempt to enhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these critical segments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion of extracted features of all three biometrics features have been used to bring in the multimodal classification approach to get fusion vectors. Once fusion vectors ware formulated, the feature level fusion technique is incorporated based on the extracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATM security. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has been selected based on the effectiveness of the features. The proposed approach results were compared with existing algorithms based on the classification accuracy to prove the effectiveness of our algorithm. Moreover, comparative results of the proposed method stand as a proof of more promising outcomes by combining the three biometric features.     

像素域Wyner-Ziv视频编码的码率控制

Wyner-Ziv视频编码通常利用反馈信道在解码端执行码率控制,造成解码复杂度很大,限制了其实际的应用.在分析与码率相关的特征量的基础上,提出一种像素域Wyner-Ziv视频编码系统的码率控制算法.首先使用相关噪声模型分布参数估计运动量的大小,然后利用位平面的时间相关性确定解码当前位平面时编码端需要传送的初始码率,从而使解码反馈次数减小,降低解码复杂度.实验结果表明,文中算法在保证编码效率的同时使解码反馈请求次数减少到原解码端码率控制算法的30%左右.     

Metacarpophalangeal joint patterns based personal identification system

This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns (MJPs). A discriminative common vector (DCV) based method is utilized for feature selection. In the literature, there is no study using whole MJP for identity authentication, exceptionally a work (Ferrer et al., 2005) using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study. The originality of this approach is that: whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP. The developed system performs some basic tasks like image acquisition, image pre-processing, feature extraction, matching, and performance evaluation. The feasibility and effectiveness of this approach is rigorously evaluated using the k-fold cross validation technique on two different databases: a publicly available database and a specially established database. The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems, DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33% and 100.00%.     

煤矿入井人员唯一性检测技术研究

通过分析掌纹、指纹、虹膜、人脸、步态、声纹等生物特征识别技术的特点以及煤矿现场对入井人员生物特征的影响,指出虹膜识别、人脸识别、步态识别、声纹识别适用于煤矿入井人员唯一性检测;提出了一种基于人员定位和生物特征识别的煤矿入井人员唯一性检测技术方案,将生物特征识别技术嵌入人员定位系统,利用人员定位识别卡实现识别卡数量及人员身份的唯一性检测;指出煤矿入井人员唯一性检测技术的研究关键点是严重污染人脸的识别算法、对设备遮挡情况下人员步态图像的采集及对混入人员语音信号的煤矿现场噪声消除算法。