Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.     

Fast periocular authentication in handheld devices with reduced phase intensive local pattern

To ensure highest security in handheld devices, biometric authentication has emerged as a reliable methodology. Deployment of mobile biometric authentication struggles due to computational complexity. For a fast response from a mobile biometric authentication method, it is desired that the feature extraction and matching should take least time. In this article, the periocular region captured through frontal camera of a mobile device is considered under investigation for its suitability to produce a reduced feature that takes least time for feature extraction and matching. A recently developed feature Phase Intensive Local Pattern (PILP) is subjected to reduction giving birth to a feature termed as Reduced PILP (R-PILP), which yields a matching time speed-up of 1.56 times while the vector is 20% reduced without much loss in authentication accuracy. The same is supported by experiment on four publicly available databases. The performance is also compared with one global feature: Phase Intensive Global Pattern, and three local features: Scale Invariant Feature Transform, Speeded-up Robust Features, and PILP. The amount of reduction can be varied with the requirement of the system. The amount of reduction and the performance of the system bears a trade-off. Proposed R-PILP attempts to make periocular suitable for mobile devices.     

基于选择子带提升位平面的ROI编码方法研究

首先分析了JPEG2000中两种ROI编码机制的优缺点,然后对图像小波变换后各子带内ROI系数的能量情况进行统计分析,提出了基于小波变换后的ROI掩膜能量选取提升重要子带,同时时重要子带采用适应小渡"零树"特性的子带扩充,进而提出了一种基于选择子带提升位平面的ROI编码方法.该算法有以下优点:(1)能够控制ROI和ROB的相对压缩质量;(2)可以获得同一图像中多ROI的不同压缩率;(3)允许定义任意形状的ROI且无须在码流中包含形状信息.实验结果验证了所提方法的有效性.     

Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment

There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.

     

结合目标检测与匹配修正的手腕骨兴趣区域提取

目的 在骨龄智能评估研究中,如何准确地提取手腕参照骨的兴趣区域(region of interest, ROI)是保证骨龄精确评估的关键。基于传统深度学习的方法用于手腕骨ROI提取,存在个别参照骨漏判、误判等情况,导致平均提取准确率较低。本文结合目标检测强大的定位和识别能力,以准确提取所有手腕骨ROI为目的,提出了一种参照骨自动匹配与修正方法。方法 针对不同参照骨形状、位置等特征表现出的规律性和关联性,本文采集了大量不同性别、不同年龄段的人手腕图谱作为参照骨样本匹配,然后分多个阶段提取参照骨ROI:1)基于目标检测算法初步提取出所有参照骨候选ROI,并根据一定的阈值剔除置信度较低的区域;2)结合参照骨的大数据样本构建位置点匹配模型,对剔除区域进行自动匹配与填补,保证ROI提取的完整性;3)通过多尺度滑动窗口以及ROI分类模型,对填补得到的ROI位置进行滑动修正,进一步保证提取准确率。结果 实验结果表明,本文结合目标检测与匹配修正的方法优于现有绝大多数方法。其中,匹配修正方法在目标检测算法的提取结果基础上,提升了约1.42%的平均准确率,当结合Faster R-CNN(region-co...     

Personal authentication using digital retinal images

Traditional authentication (identity verification) systems, used to gain access to a private area in a building or to data stored in a computer, are based on something the user has (an authentication card, a magnetic key) or something the user knows (a password, an identification code). However, emerging technologies allow for more reliable and comfortable user authentication methods, most of them based on biometric parameters. Much work could be found in the literature about biometric-based authentication, using parameters like iris, voice, fingerprints, face characteristics, and others. In this work a novel authentication method is presented and preliminary results are shown. The biometric parameter employed for the authentication is the retinal vessel tree, acquired through retinal digital images, i.e., photographs of the fundus of the eye. It has already been asserted by expert clinicians that the configuration of the retinal vessels is unique for each individual and that it does not vary during his life, so it is a very well-suited identification characteristic. Before the verification process can be executed, a registration step is required to align both the reference image and the picture to be verified. A fast and reliable registration method is used to perform this step, so that the whole authentication process takes about 0.3 s.     

基于ISA-DWT的多个任意形状感兴趣区域编码框架

提出了一个多个任意形状感兴趣区域编码框架.通过引入灰度掩膜图像来表示不同感兴趣区域的优先级,在总的目标码率和各个优先级感兴趣区域重建质量的约束条件下,按照优先级从高到低的顺序对不同的感兴趣区域采用整数到整数形状自适应离散小波进行变换,对变换后的系数采用修改的SPIHT算法产生单独的压缩码流.实验结果表明,本编码框架有如下优点：（1）根据感兴趣区域的优先级顺序来保证感兴趣区域的目标重建质量;（2）适合于任意形状感兴趣区域的有损和无损压缩;（3）感兴趣区域重建质量估计方法简单有效.     

Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback

Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human perception. The feedback information is utilized to discover the subjective ROI perception of a particular user, and it is further employed to recompute the features associated with ROIs with the updated personalized ROI preference. A fast computation technique is proposed to avoid repeating the ROI detection for images in the database. It directly estimates the features of the ROIs, which makes the query process fast and efficient. For illustration of the overall approach, we use the color saliency and wavelet feature saliency to determine the ROIs. Normalized projections are selected to represent the shape features associated with the ROIs. Experimental results show that the proposed system has better performance than the global features based approaches and region based techniques without feedback.     

Secure multimodal biometric authentication with wavelet quantization based fingerprint watermarking

As malicious attacks greatly threaten the security and reliability of biometric systems, ensuring the authenticity of biometric data is becoming increasingly important. In this paper we propose a watermarking-based two-stage authentication framework to address this problem. During data collection, face features are embedded into a fingerprint image of the same individual as data credibility token and secondary authentication source. At the first stage of authentication, the credibility of input data is established by checking the validness of extracted patterns. Due to the specific characteristics of face watermarks, the face detection based classification strategies are introduced for reliable watermark verification instead of conventional correlation based watermark detection. If authentic, the face patterns can further serve as supplemental identity information to facilitate subsequential biometric authentication. In this framework, one critical issue is to guarantee the robustness and capacity of watermark while preserving the discriminating features of host fingerprints. Hence a wavelet quantization based watermarking approach is proposed to adaptively distribute watermark energy on significant DWT coefficients of fingerprint images. Experimental results which evaluate both watermarking and biometric authentication performance demonstrate the effectiveness of this work.     

Ocular recognition databases and competitions: a survey

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

     

Privacy-preserving continuous authentication using behavioral biometrics

Continuous authentication modalities collect and utilize users’ sensitive data to authenticate them continuously. Such data contain information about user activities, behaviors, and other demographic information, which causes privacy concerns. In this paper, we propose two privacy-preserving protocols that enable continuous authentication while preventing the disclosure of user-sensitive information to an authentication server. We utilize homomorphic cryptographic primitives that protect the privacy of biometric features with an oblivious transfer protocol that enables privacy-preserving information retrieval. We performed the biometric evaluation of the proposed protocols on two datasets, a swipe gesture dataset and a keystroke dynamics dataset. The biometric evaluation shows that the protocols have very good performance. The execution time of the protocols is measured by considering continuous authentication using: only swipe gestures, keystroke dynamics, and hybrid modalities. The execution time proves the protocols are very efficient, even on high-security levels.

     

Periocular recognition: how much facial expressions affect performance?

Using information near the human eye to perform biometric recognition has been gaining popularity. Previous works in this area, designated periocular recognition, show remarkably low error rates and particularly high robustness when data are acquired under less controlled conditions. In this field, one factor that remains to be studied is the effect of facial expressions on recognition performance, as expressions change the textural/shape information inside the periocular region. We have collected a multisession dataset whose single variation is the subjects’ facial expressions and analyzed the corresponding variations in performance, using the state-of-the-art periocular recognition strategy. The effectiveness attained by different strategies to handle the effects of facial expressions was compared: (1) single-sample enrollment; (2) multisample enrollment, and (3) multisample enrollment with facial expression recognition, with results also validated in the well-known Cohn–Kanade AU-Coded Expression dataset. Finally, the role of each type of facial expression in the biometrics menagerie effect is discussed.     

A review on performance,security and various biometric template protection schemes for biometric authentication systems

Identifying a person based on their behavioral and biological qualities in an automated manner is called biometrics. The authentication system substituting traditional password and token for authentication and relies gradually on biometric authentication methods for verification of the identity of an individual. This proves the fact that society has started depending on biometric-based authentication systems. Security of biometric authentication needs to be reviewed and discussed as there are multiple points related to integrity and public reception of biometric-based authentication systems. Security and recognition accuracy are the two most important aspects which must be considered while designing biometric authentication systems. During enrollment phase scanning of biometric data is done to determine a set of distinct biometric feature set known as biometric template. Protection of biometric templates from various hacking efforts is a topic of vital importance as unlike passwords or tokens, compromised biometric templates cannot be reissued. Therefore, giving powerful protection techniques for biometric templates and still at that very moment preparing great identification accuracy is a good research problem nowadays, as well as in the future. Furthermore, efficiency under non-ideal conditions is also supposed to be inadequate and thus needs special attention in the design of a biometric authentication system. Disclosure of various biometric traits in miscellaneous applications creates a severe compromise on the privacy of the user. Biometric authentication can be utilized for remote user authentication. In this case, the biometric data of users typically called templates are stored in a server. The uniqueness and stability of biometrics ended it useful over traditional authentication systems. But, a similar thing made the enduring harm of a user’s identity in biometric systems. The architecture of the biometric system leads to several hazards that lead to numerous security concerns and privacy threats. To address this issue, biometric templates are secured using several schemes that are categorized as biometric cryptosystems, cancelable biometrics, hybrid methods, Homomorphic Encryption, visual cryptography based methods. Biometric cryptosystems and cancelable biometrics techniques provide reliable biometric security at a great level. However, there persist numerous concerns and encounters that are being faced during the deployment of these protection technologies. This paper reviews and analyses various biometric template protection methods. This review paper also reflects the limitations of various biometric template protection methods being used in present times and highlights the scope of future work.

     

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。     

Robust memory-efficient data level information fusion of multi-modal biometric images

This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image. The proposed approach reduces the memory size, increases the recognition accuracy using multi-modal biometric features, and withstands common attacks such as smoothing, cropping, JPEG 2000, and filtering due to tampering. The fusion algorithm is validated using the verification algorithms we developed, existing algorithms, and commercial algorithm. In addition to our multi-modal database, experiments are also performed on other well known databases such as FERET face database and CASIA iris database. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The results show that the fusion process reduced the memory required for storing the multi-modal images by 75%. The integrity of biometric features and the recognition performance of the resulting composite fused image is not affected significantly. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. We also propose a multi-modal biometric algorithm that further reduces the equal error rate compared to individual biometric images.     

Bi-modal biometric authentication on mobile phones in challenging conditions

This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.     

A hybrid encryption/hiding method for secure transmission of biometric data in multimodal authentication system

Biometric security is a fast growing area that gains an increasing interest in the last decades. Digital encryption and hiding techniques provide an efficient solution to protect biometric data from accidental or intentional attacks. In this paper, a highly secure encryption/hiding scheme is proposed to ensure secure transmission of biometric data in multimodal biometric identification/authentication system. The secret fingerprint and iris vectors are sparsely approximated using accelerated iterative hard thresholding technique and then embedded in the host Slantlet-SVD domain of face image. Experiments demonstrate the efficiency of our technique for both encryption and hiding purpose, where the secret biometric information is well encrypted and still extractable with high fidelity even though the carrier image is seriously corrupted. Our experimental results show the efficiency of the proposed technique in term of robustness to attacks, Invisibility, and security.

     

掌纹ROI分割算法的研究与实现

掌纹感兴趣区(ROI)分割是掌纹识别的关键步骤,目前掌纹分割方法主要存在定位点不易确定和同类图像ROI提取偏移度较大等问题,为改善这些问题,提出一种新的ROI分割算法。首先确定手掌图像中的两个指谷点;然后利用手掌轮廓特定区域边界点拟合直线,以该直线为基准,以固定角度的方式建立直角坐标系,利用指谷点找到掌纹信息丰富的区域,确定掌纹的ROI,最后提取特征矢量进行匹配识别。实验结果表明,该算法分割掌纹ROI的准确度高、速度快,对同类图像分割的偏移度更小,掌纹ROI的提取率达98.2%,掌纹正确识别率提高了3%左右,为基于掌纹的身份认证系统的实现提供了理论和实验依据。     

Identifying Noncooperative Subjects at a Distance Using Face Images and Inferred Three-Dimensional Face Models

We present an approach to identify noncooperative individuals at a distance from a sequence of images, using 3-D face models. Most biometric features (such as fingerprints, hand shape, iris, or retinal scans) require cooperative subjects in close proximity to the biometric system. We process images acquired with an ultrahigh-resolution video camera, infer the location of the subjects' head, use this information to crop the region of interest, build a 3-D face model, and use this 3-D model to perform biometric identification. To build the 3-D model, we use an image sequence, as natural head and body motion provides enough viewpoint variation to perform stereomotion for 3-D face reconstruction. We have conducted experiments on a 2-D and 3-D databases collected in our laboratory. First, we found that metric 3-D face models can be used for recognition by using simple scaling method even though there is no exact scale in the 3-D reconstruction. Second, experiments using a commercial 3-D matching engine suggest the feasibility of the proposed approach for recognition against 3-D galleries at a distance (3, 6, and 9 m). Moreover, we show initial 3-D face modeling results on various factors including head motion, outdoor lighting conditions, and glasses. The evaluation results suggest that video data alone, at a distance of 3 to 9 meters, can provide a 3-D face shape that supports successful face recognition. The performance of 3-D–3-D recognition with the currently generated models does not quite match that of 2-D–2-D. We attribute this to the quality of the inferred models, and this suggests a clear path for future research.      

Ocular biometrics in the visible spectrum: A survey

Ocular biometrics encompasses the imaging and use of characteristic features extracted from the eyes for personal recognition. Ocular biometric modalities in visible light have mainly focused on iris, blood vessel structures over the white of the eye (mostly due to conjunctival and episcleral layers), and periocular region around eye. Most of the existing studies on iris recognition use the near infrared spectrum. However, conjunctival vasculature and periocular regions are imaged in the visible spectrum. Iris recognition in the visible spectrum is possible for light color irides or by utilizing special illumination. Ocular recognition in the visible spectrum is an important research area due to factors such as recognition at a distance, suitability for recognition with regular RGB cameras, and adaptability to mobile devices. Further these ocular modalities can be obtained from a single RGB eye image, and then fused together for enhanced performance of the system. Despite these advantages, the state-of-the-art related to ocular biometrics in visible spectrum is not well known. This paper surveys this topic in terms of computational image enhancement, feature extraction, classification schemes and designed hardware-based acquisition set-ups. Future research directions are also enumerated to identify the path forward.