Periocular-based biometrics robust to eye rotation based on polar coordinates

Conventional iris recognition requires a high-resolution camera equipped with a zoom lens and a near-infrared illuminator to observe iris patterns. Moreover, with a zoom lens, the viewing angle is small, restricting the user’s head movement. To address these limitations, periocular recognition has recently been studied as biometrics. Because the larger surrounding area of the eye is used instead of iris region, the camera having the high-resolution sensor and zoom lens is not necessary for the periocular recognition. In addition, the image of user’s eye can be captured by using the camera having wide viewing angle, which reduces the constraints to the head movement of user’s head during the image acquisition. Previous periocular recognition methods extract features in Cartesian coordinates sensitive to the rotation (roll) of the eye region caused by in-plane rotation of the head, degrading the matching accuracy. Thus, we propose a novel periocular recognition method that is robust to eye rotation (roll) based on polar coordinates. Experimental results with open database of CASIA-Iris-Distance database (CASIA-IrisV4) show that the proposed method outperformed the others.     

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.     

Enhanced near-infrared periocular recognition through collaborative rendering of hand crafted and deep features

Periocular recognition leverage from larger feature region and lesser user cooperation, when compared against the traditional iris recognition. Moreover, in the current scenario of Covid-19, where majority of people cover their faces with masks, potential of recognizing faces gets reduced by a large extent, calling for wide applicability of periocular recognition. In view of these facts, this paper targets towards enhanced representation of near-infrared periocular images, by combined use of hand-crafted and deep features. The hand-crafted features are extracted through partitioning of periocular image followed by obtaining the local statistical properties pertaining to each partition. Whereas, deep features are extracted through the popular convolutional neural network (CNN) ResNet-101 model. The extensive set of experiments performed with a benchmark periocular database validates the promising performance of the proposed method. Additionally, investigation of cross-spectral matching framework and comparison with state-of-the-art, reveal that combination of both types of features employed could prove to be extremely effective.

     

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.

     

基于虹膜纹理深度特征和Fisher向量的人种分类

目的 虹膜是位于人眼表面黑色瞳孔和白色巩膜之间的圆环形区域,有着丰富的纹理信息。虹膜纹理具有高度的区分性和稳定性。人种分类是解决虹膜识别在大规模数据库上应用难题的主要方法之一。现有的虹膜图像人种分类方法主要采用手工设计的特征,而且针对亚洲人和非亚洲人的基本人种分类,无法很好地解决亚种族分类问题。为此提出一种基于虹膜纹理深度特征和Fisher向量的人种分类方法。方法 首先用CNN（convolutional neural network）对归一化后的虹膜纹理图像提取深度特征向量,作为底层特征;然后使用高斯混合模型提取Fisher向量作为最终的虹膜特征表达;最后用支持向量机分类得到最终结果。结果 本文方法在亚洲人和非亚洲人的数据集上采用non-person-disjoint的方式取得99.93%的准确率,采用person-disjoint的方式取得91.94%的准确率;在汉族人和藏族人的数据集上采用non-person-disjoint的方式取得99.69%的准确率,采用person-disjoint的方式取得82.25%的准确率。结论 本文通过数据驱动的方式从训练数据中学习到更适合人种分类的特征,可以很好地实现对基本人种以及亚种族人种的分类,提高了人种分类的精度。同时也首次证明了用虹膜图像进行亚种族分类的可行性,对人种分类理论进行了进一步地丰富和完善。     

一种新的基于完整相位分析的虹膜分类算法

将预处理后的虹膜图像分成64幅子块,并分别进行特征提取,然后按顺序提取每幅子图像完整的相位信息,建立相位特征向量,最后通过对特征向量进行内积运算获得虹膜样本之间的归一化分类距离。实验结果表明,该算法思路简洁,识别效果好,为虹膜分类研究提供了一种新的参考途径。     

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/

     

High performance iris recognition based on 1-D circular feature extraction and PSO–PNN classifier

In this paper, a novel iris feature extraction technique with intelligent classifier is proposed for high performance iris recognition. We use one dimensional circular profile to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1-D wavelet transform. So as to improve the accuracy, this paper combines probabilistic neural network (PNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. A comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The experimental results reveal the proposed algorithm provides superior performance in iris recognition.     

IrisCodeNet：虹膜特征编码网络

使用有效的特征提取算法对虹膜纹理进行准确的表达是虹膜识别技术的关键。基于虹膜识别任务的特殊性,提出了用于虹膜特征编码的网络模型IrisCodeNet。该网络架构使用了改进的BasicBlock,并结合了可以扩大决策边界的损失函数AM-Softmax（additive margin softmax）。为了获取最佳的虹膜识别效果,对AM-Softmax的参数设置、虹膜图像预处理输入形式、数据增强方式、网络输入尺寸做了细致的研究。实验结果表明：使用IrisCodeNet训练得到的特征提取器在CASIA-Iris-Thousand、CASIA-Iris-Distance、IITD虹膜数据库上进行测试,所评估的等错误率（equal error rate,EER）和正确接受率（true acceptance rate,TAR）均远远超过了广泛应用的传统算法。特别地,IrisCodeNet无需传统的虹膜归一化或精确的虹膜分割步骤依然取得了极好的识别效果。并且使用Grad-CAM（gradient-weighted class activation mapping）算法进行了可视化分析,结果表明该网络框架有效地关注了虹膜纹理信息,从而证明了IrisCodeNet具有较强的虹膜纹理特征提取能力。     

Useful features for human verification in near-infrared periocular images

The periocular region is the part of the face immediately surrounding the eye, and researchers have recently begun to investigate how to use the periocular region for recognition. Understanding how humans recognize faces helped computer vision researchers develop algorithms for face recognition. Likewise, understanding how humans analyze periocular images could benefit researchers developing algorithms for periocular recognition. We conducted two experiments to determine how humans analyze periocular images. In these experiments, we presented pairs of images and asked volunteers to determine whether the two images showed eyes from the same subject or from different subjects. In the first experiment, subjects were paired randomly to create different-subject queries. Our volunteers correctly determined the relationship between the two images in 92% of the queries. In the second experiment, we considered multiple factors in forming different-subject pairs; queries were formed from pairs of subjects with the same gender and race, and with similar eye color, makeup, eyelash length, and eye occlusion. In addition, we limited the amount of time volunteers could view a query pair. On this harder experiment, the correct verification rate was 79%. We asked volunteers to describe what features in the images were helpful to them in making their decisions. In both experiments, eyelashes were reported to be the most helpful feature.     

Multi-instance cancellable biometrics schemes based on generative adversarial network

The main role of cancellable biometric schemes is to protect the privacy of the enrolled users. The protected biometric data are generated by applying a parametrized transformation function to the original biometric data. Although cancellable biometric schemes achieve high security levels, they may degrade the recognition accuracy. One of the mostwidely used approaches to enhance the recognition accuracy in biometric systems is to combine several instances of the same biometric modality. In this paper, two multi-instance cancellable biometric schemes based on iris traits are presented. The iris biometric trait is used in both schemes because of the reliability and stability of iris traits compared to the other biometric traits. A generative adversarial network (GAN) is used as a transformation function for the biometric features. The first scheme is based on a pre-transformation feature-level fusion, where the binary features of multiple instances are concatenated and inputted to the transformation phase. On the other hand, the second scheme is based on a post-transformation feature-level fusion, where each instance is separately inputted to the transformation phase. Experiments conducted on the CASIA Iris-V3-Internal database confirm the high recognition accuracy of the two proposed schemes. Moreover, the security of the proposed schemes is analyzed, and their robustness against two well-known types of attacks is proven.

     

Ordinal Measures for Iris Recognition

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.     

区域注意力机制引导的双路虹膜补全

目的 虹膜识别是一种稳定可靠的生物识别技术,但虹膜图像的采集过程会受到多种干扰造成图像中虹膜被遮挡,比如光斑遮挡、上下眼皮遮挡等。这些遮挡的存在,一方面会导致虹膜信息缺失,直接影响虹膜识别的准确性,另一方面会影响预处理(如定位、分割)的准确性,间接影响虹膜识别的准确性。为解决上述问题,本文提出区域注意力机制引导的双路虹膜补全网络,通过遮挡区域的像素补齐,可以显著减少被遮挡区域对虹膜图像预处理和识别的影响,进而提升识别性能。方法 使用基于Transformer的编码器和基于卷积神经网络(convolutional neural network, CNN)的编码器提取虹膜特征,通过融合模块将两种不同编码器提取的特征进行交互结合,并利用区域注意力机制分别处理低层和高层特征,最后利用解码器对处理后的特征进行上采样,恢复遮挡区域,生成完整图像。结果 在CASIA(Institute of Automation, Chinese Academy of Sciences)虹膜数据集上对本文方法进行测试。在虹膜识别性能方面,本文方法在固定遮挡大小为64×64像素的情况下,遮挡补全结果的TAR(true accept rate)(0.1%FAR(false accept rate))为63%,而带有遮挡的图像仅为19.2%,提高了43.8%。结论 本文所提出的区域注意力机制引导的双路虹膜补全网络,有效结合Transformer的全局建模能力和CNN的局部建模能力,并使用针对遮挡的区域注意力机制,实现了虹膜遮挡区域补全,进一步提高了虹膜识别的性能。     

Cross-sensor periocular biometrics in a global pandemic: Comparative benchmark and novel multialgorithmic approach

The massive availability of cameras and personal devices results in a wide variability between imaging conditions, producing large intra-class variations and a significant performance drop if images from heterogeneous environments are compared for person recognition purposes. However, as biometric solutions are extensively deployed, it will be common to replace acquisition hardware as it is damaged or newer designs appear or to exchange information between agencies or applications operating in different environments. Furthermore, variations in imaging spectral bands can also occur. For example, face images are typically acquired in the visible (VIS) spectrum, while iris images are usually captured in the near-infrared (NIR) spectrum. However, cross-spectrum comparison may be needed if, for example, a face image obtained from a surveillance camera needs to be compared against a legacy database of iris imagery. Here, we propose a multialgorithmic approach to cope with periocular images captured with different sensors. With face masks in the front line to fight against the COVID-19 pandemic, periocular recognition is regaining popularity since it is the only region of the face that remains visible. As a solution to the mentioned cross-sensor issues, we integrate different biometric comparators using a score fusion scheme based on linear logistic regression This approach is trained to improve the discriminating ability and, at the same time, to encourage that fused scores are represented by log-likelihood ratios. This allows easy interpretation of output scores and the use of Bayes thresholds for optimal decision-making since scores from different comparators are in the same probabilistic range. We evaluate our approach in the context of the 1st Cross-Spectral Iris/Periocular Competition, whose aim was to compare person recognition approaches when periocular data from visible and near-infrared images is matched. The proposed fusion approach achieves reductions in the error rates of up to 30%–40% in cross-spectral NIR–VIS comparisons with respect to the best individual system, leading to an EER of 0.2% and a FRR of just 0.47% at FAR = 0.01%. It also represents the best overall approach of the mentioned competition. Experiments are also reported with a database of VIS images from two different smartphones as well, achieving even bigger relative improvements and similar performance numbers. We also discuss the proposed approach from the point of view of template size and computation times, with the most computationally heavy comparator playing an important role in the results. Lastly, the proposed method is shown to outperform other popular fusion approaches in multibiometrics, such as the average of scores, Support Vector Machines, or Random Forest.     

A novel cancellable Iris template generation based on salting approach

The iris has been vastly recognized as one of the powerful biometrics in terms of recognition performance, both theoretically and empirically. However, traditional unprotected iris biometric recognition schemes are highly vulnerable to numerous privacy and security attacks. Several methods have been proposed to generate cancellable iris templates that can be used for recognition; however, these templates achieve lower accuracy of recognition in comparison to traditional unprotected iris templates. In this paper, a novel cancellable iris recognition scheme based on the salting approach is introduced. It depends on mixing the original binary iris code with a synthetic pattern using XOR operation. This scheme guarantees a high degree of privacy/security preservation without affecting the performance accuracy compared to the unprotected traditional iris recognition schemes. Comprehensive experiments on various iris image databases demonstrate similar accuracy to those of the original counterparts. Hence, robustness to several major privacy/security attacks is guaranteed.

     

An effective approach for iris recognition using phase-based image matching

This paper presents an efficient algorithm for iris recognition using phase-based image matching --- an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (ver. 1.0 and ver. 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art DSP (Digital Signal Processing) technology.     

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.     

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.     

利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法*

目前基于2D-Gabor滤波器的虹膜识别算法主要是使用虹膜的相位信息或能量信息作为特征,但在虹膜可用区域减少时这些算法的识别效果明显下降。虹膜纹理除具有上述特征外,还有很强的方向性。在分析了2D-Gabor滤波器的方向和频率选择性后,提出了一种利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法。实验表明该方法提取的虹膜纹理方向特征可以在较小区域内提取出足够丰富的可区分性特征,实现高准确性的虹膜识别,说明方向特征是一种有效的虹膜识别特征。