Face anti-spoofing with generated near-infrared images

Reflection differences between live faces and spoof faces under near-infrared spectrum make near-infrared image based methods obtain superior performance for face anti-spoofing. However, for conventional face recognition systems, near-infrared image based methods need additional near-infrared equipment to capture the input near-infrared images. In this paper, we propose a novel face anti-spoofing method which exploits the clues in both visible light (VIS) images and near-infrared (NIR) images without utilizing any near-infrared equipment during testing. Specifically, we first propose a novel multiple categories image translation generative adversarial network (MCT-GAN) which generates corresponding NIR images for VIS live and spoof face images. Then we utilize convolution neural network to learn fusing features from both VIS images and corresponding generated NIR images for the goal of live and spoof face classification. Qualitative and quantitative experiments demonstrate that our method obtains excellent results compared to the state-of-the-art methods.

     

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.     

Fusing the information in visible light and near-infrared images for iris recognition

Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.     

基于稀疏表征的可见光和近红外光人脸图像融合快速识别算法

近年来,融合可见光和近红外光的人脸图像特征识别成为一个研究热点。对该领域中的快速人脸识别技术进行研究,并给出了一个具体的实现方案。该方案主要包括以下3种技术:原始样本的下采样;基于稀疏表征原理,选取测试样本的M近邻来代替原始训练样本;加权决策融合。在CSIST人脸库上的实验结果表明,和同类算法相比,所提算法在识别率和计算速度上均有提高。     

An optimal feature enriched region of interest (ROI) extraction for periocular biometric system

With the onset of COVID-19 pandemic, wearing of face mask became essential and the face occlusion created by the masks deteriorated the performance of the face biometric systems. In this situation, the use of periocular region (region around the eye) as a biometric trait for authentication is gaining attention since it is the most visible region when masks are used. One important issue in periocular biometrics is the identification of an optimal size periocular ROI which contains enough features for authentication. The state of the art ROI extraction algorithms use fixed size rectangular ROI calculated based on some reference points like center of the iris or centre of the eye without considering the shape of the periocular region of an individual. This paper proposes a novel approach to extract optimum size periocular ROIs of two different shapes (polygon and rectangular) by using five reference points (inner and outer canthus points, two end points and the midpoint of eyebrow) in order to accommodate the complete shape of the periocular region of an individual. The performance analysis on UBIPr database using CNN models validated the fact that both the proposed ROIs contain enough information to identify a person wearing face mask.

     

Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance.     

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.     

Towards enhancing the security and accuracy of iris recognition systems

The intricate structure of the iris constitutes a powerful biometric characteristic utilized by iris recognition algorithms to extract discriminative biometric templates. Iris recognition is field-proven but consequential issues, e.g. privacy protection or recognition in unconstrained environments, still to be solved, raise the need for further investigations. In this paper different improvements focused on template protection and biometric comparators are presented. Experimental evaluations are performed on a public dataset confirming the soundness of proposed enhancements.     

Recognition in the near infrared spectrum for face,gender and facial expressions

Visible face recognition systems are subjected to failure when recognizing the faces in unconstrained scenarios. So, recognizing faces under variable and low illumination conditions are more important since most of the security breaches happen during night time. Near Infrared (NIR) spectrum enables to acquire high quality images, even without any external source of light and hence it is a good method for solving the problem of illumination. Further, the soft biometric trait, gender classification and non verbal communication, facial expression recognition has also been addressed in the NIR spectrum. In this paper, a method has been proposed to recognize the face along with gender classification and facial expression recognition in NIR spectrum. The proposed method is based on transfer learning and it consists of three core components, i) training with small scale NIR images ii) matching NIR-NIR images (homogeneous) and iii) classification. Training on NIR images produce features using transfer learning which has been pre-trained on large scale VIS face images. Next, matching is performed between NIR-NIR spectrum of both training and testing faces. Then it is classified using three, separate SVM classifiers, one for face recognition, the second one for gender classification and the third one for facial expression recognition. It has been observed that the method gives state-of-the-art accuracy on the publicly available, challenging, benchmark datasets CASIA NIR-VIS 2.0, Oulu-CASIA NIR-VIS, PolyU, CBSR, IIT Kh and HITSZ for face recognition. Further, for gender classification the Oulu-CASIA NIR-VIS, PolyU,and IIT Kh has been analyzed and for facial expression the Oulu-CASIA NIR-VIS dataset has been analyzed.

     

基于Adaboost的人脸与虹膜融合识别

传统生物特征识别系统的识别率经常受到环境以及生物学特征的自身局限性影响。针对该不足,提出一种基于人脸与虹膜特征级融合的多模态生物识别系统,采用中心对称局部二值模式算子提取人脸和虹膜的纹理特征,将人脸特征与虹膜特征线性整合成混合特征向量,利用Adaboost算法从该混合特征向量中优选出一组最佳特征组合,从而构成强分类器。实验结果表明,该多模态系统相比单模态系统具有更好的鲁棒性。     

结构特征下的可撤销人脸识别

目的 相对于其他生物特征识别技术,人脸识别具有非接触、不易察觉和易于推广等特点,在公共安全和日常生活中得到广泛应用。在移动互联网时代,云端人脸识别可以有效地提高识别精度,但是需要将大量的人脸数据上传到第三方服务器。由于人的面部特征是唯一的,一旦数据库泄露就会面临模板攻击和假冒攻击等安全威胁。为了保证人脸识别系统的安全性并提高其识别率,本文提出一种融合人脸结构特征的可撤销人脸识别算法。方法 首先,对原始人脸图像提取结构特征作为虚部分量,与原始人脸图像联合构建复数矩阵并通过随机二值矩阵进行置乱操作。然后,使用2维主成分分析方法将置乱的复数矩阵映射到新的特征空间。最后,采用基于曼哈顿距离的最近邻分类器计算识别率。结果 在4个不同人脸数据库上的实验结果表明,原始人脸图像和结构特征图像经过随机二值矩阵置乱后,人眼无法察觉出有用的信息且可以重新生成,而且融合方差特征后,在GT （Georgia Tech）、NIR （Near Infrared）、VIS （Visible Light）和YMU （YouTuBe Makeup）人脸数据库上,平均人脸识别率分别提高了4.9%、2.25%、2.25%和1.98%,且平均测试时间均在1.0 ms之内,表明该算法实时性强,能够满足实际应用场景的需求。结论 本文算法可在不影响识别率的情况下保证系统的安全性,满足可撤销性。同时,融合结构特征丰富了人脸信息的表征,提高了人脸识别系统的识别率。     

VISA: a multimodal database of face and iris traits

In this paper, a new realistic and challenging Face-Iris multimodal biometric database called VISA database is described. One significant problem associated with the development and evaluation of multimodal biometric systems using face and iris biometric traits is the lack of publicly available multimodal databases that are acquired in an unconstrained environment. Currently, there exist no multimodal databases containing a sufficient number of common subjects involved in both face and iris data acquisition process under different conditions. The VISA database fulfills these requirements and it will be a useful tool for the design and development of new algorithms for developing multimodal biometric systems. The VISA iris images are acquired using the IriShield camera. Face images are captured using mobile device. The corpus of a new VISA database consists of face images that vary in expression, pose and illumination, and presence of occlusion whereas iris images vary in illumination, eye movement, and occlusion. A total of more than 5000 images of 100 subjects are collated and used to form the new database. The key features of the VISA dataset are the wide and diverse population of subjects (age and gender). The VISA database is able to support face and/or iris unimodal or multimodal biometric recognition. Hence, the VISA database is a useful addition for the purpose of research and development of biometric systems based on face and iris biometrics. This paper also describes the baseline results of state-of-the-art methods on the VISA dataset and other popular similar datasets. The VISA database will be made available to the public through https://vtu.ac.in/en/visa-multimodal-face-and-iris-biometrics-database/

     

Feature-domain super-resolution for iris recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the acquired iris images, which significantly degrades iris recognition performance. Super-resolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, most existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values, rather than the actual features used for recognition. This paper thoroughly investigates transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. A framework for applying super-resolution to nonlinear features in the feature-domain is proposed. Based on this framework, a novel feature-domain super-resolution approach for the iris biometric employing 2D Gabor phase-quadrant features is proposed. The approach is shown to outperform its pixel domain counterpart, as well as other feature domain super-resolution approaches and fusion techniques.     

Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition

Recently, multi-modal biometric fusion techniques have attracted increasing atove the recognition performance in some difficult biometric problems. The small sample biometric recognition problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion recognition approach is a rather effective solution for the small sample recognition problem.     

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.

     

基于比特流的虹膜特征数据的隐藏算法

对生物特征数据的攻击是生物特征识别自身安全的主要威胁。为了提高虹膜特征数据的安全性,根据现有主要的虹膜识别方法中特征模板的数据特性和基于汉明距的比对方法,提出一种基于比特流的将虹膜特征模板数据嵌入人脸图像的数据隐藏算法。实验结果表明,该算法具有较强的隐蔽性,隐藏算法本身误码率为零,计算效率高,不会影响虹膜识别技术本身的性能,能够有效保护特征模板数据,增强虹膜识别系统自身的安全性。     

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.     

LAMP-HQ: A Large-Scale Multi-pose High-Quality Database and Benchmark for NIR-VIS Face Recognition

International Journal of Computer Vision - Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images...     

On designing practical long range near infrared-based face recognition systems

Although automated face recognition (AFR) is a well-studied problem with a history of more than three decades, it is still far from being considered a solved problem for the case of difficult exposure conditions, such as during night-time, in environments with unconstrained lighting, or at large distances from the camera. However, in practical forensic scenarios, it is often the case that investigators operate in difficult conditions, where cross-session data need to be matched and where, grouping of the data in the context of demographic information (constitute the grouping in terms of gender, ethnicity) may be used in order to assist law enforcement officials, forensic investigators and security personnel in human identification practices. In this paper, we discuss the challenges in designing a practical near infrared (NIR) FR system and, more specifically, study the problems of intra-spectral, cross-spectral, i.e. VIS–NIR, intra-distance and cross-distance NIR FR, in indoors, outdoors, day-time and night-time environments. Furthermore, we propose the usage of a multi-feature scenario dependent fusion scheme that can enhance recognition performance. We also investigate which scenarios used, related to datasets, features useful for face matching or their combination, are most beneficial to the identification accuracy of NIR FR systems, when the gallery set is composed of either visible or NIR band face images. Thus, we illustrate that the selection of specific feature extraction techniques and their fusion are often the key design aspects that can turn practically non-functional systems to effective systems with real-world applicability. As a result, such a strategy can significantly extend the range of conditions under which automated NIR FR systems can operate.