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Online biometric authentication using facial thermograms
Abstract
The requirement for a reliable personal identification in computerized access control, security applications, human machine interaction etc has led to an unprecedented interest in biometrics. The usefulness of face as a primary modality for biometric authentication is on the rise in the recent years because of it's non-intrusiveness and uniqueness. Visual Face recognition is successful only in the controlled environment but fails in the case of disguised faces and under varying lighting conditions. As an alternative to the visual recognition this paper presents the Long Wave Infra Red (LWIR) for face recognition. In this we make of the facial thermograms that are the images formed by the capturing the heat radiated by the face. It is observed that it's performance falls drastically with varying temperature conditions. To overcome this drawback simplified Blood perfusion model is proposed to convert thermograms into Blood perfusion data. If a person wears spectacles, the glasses obstruct the radiated and hence thermograms loses the information. An efficient algorithm is developed to detect the eyeglasses and to remove it's effect.
... A comparison of the face recognition performances with four features (LBP, LDP, WLD, and HOG) by Hermosilla et al. [22] on thermal images revealed that accuracy was higher with HOG and LBP. Hanmandlu et al. [23] proposed converting thermal images of the face into simple blood perfusion data for recognition. After the conversion, the recognition is done using principal component analysis (PCA). ...
Personal authentication based on the periocular region can be performed even when the person's mouth and nose are hidden by a face mask. However, using visible light images for periocular recognition is problematic because recognition accuracy is affected by changes in the lighting conditions. We have developed a method for periocular recognition that overcomes this problem by using thermal images, which are less affected by changes in lighting conditions, in addition to visible light images. In this paper, we propose a method using both thermal and visible light images for periocular recognition based on features obtained by CNN. In addition, our method uses deep metric learning to deal with persons who are not included in the training data. To evaluate the accuracy of the proposed method under unstable conditions, we conducted recognition experiments using images of 83 subjects obtained from the USTC-NVIE database, which contains visible light and thermal images taken simultaneously under various lighting conditions and with various facial expressions. The experimental results show that using both visible light and thermal images achieves higher recognition accuracy than using only visible light images.
... Facial traits are persistent but may change and vary over time, although heat generated by the tissues of the face has a measurable repeatable pattern. It can be more stable than the facial structure [37]. Finger and palm prints and vein patterns tend to remain constant. ...
The advent of blockchain technology has led to a massive wave of different decentralized ledger technology (DLT) solutions. Such projects as Bitcoin and Ethereum have shifted the paradigm of how to transact value in a decentralized manner, but their various core technologies have their own advantages and disadvantages. This paper aims to describe an alternative to modern decentralized financial networks by introducing the Humanode network. Humanode is a network safeguarded by cryptographically secure bio-authorized nodes. Users will be able to deploy nodes by staking their encrypted biometric data. This approach can potentially lead to the creation of a public, permissionless financial network based on consensus between equal human nodes with algorithm-based emission mechanisms targeting real value growth and proportional emission. Humanode combines different technological stacks to achieve a decentralized, secure, scalable, efficient, consistent, immutable, and sustainable financial network: 1) a bio-authorization module based on cryptographically secure neural networks for the private classification of 3D templates of users' faces 2) a private Liveness detection mechanism for identification of real human beings 3) a Substrate module as a blockchain layer 4) a cost-based fee system 5) a Vortex decentralized autonomous organization (DAO) governing system 6) a monetary policy and algorithm, Fath, where monetary supply reacts to real value growth and emission is proportional. All of these implemented technologies have nuances that are crucial for the integrity of the network. In this paper we address these details, describing problems that might occur and their possible solutions. The main goal of Humanode is to create a stable and just financial network that relies on the existence of human life.
... Facial traits are persistent but may change and vary over time, although heat generated by the tissues of the face has a measurable repeatable pattern. It can be more stable than the facial structure [37]. Finger and palm prints and vein patterns tend to remain constant. ...
The pursuit of new decentralized financial networks has engulfed the world. In the last few decades, dozens of different protocol prototypes have been deployed to achieve a decentralized state of finance, but most are unable to overcome plutocratic governing systems that derive from the principles upon which Proof-of-Work (PoW) and Proof-of-Stake (PoS) heavily rely. The advent of blockchain technology has led to a massive wave of different decentralized ledger technology (DLT) solutions. Such projects as Bitcoin and Ethereum have shifted the paradigm of how to transact value in a decentralized manner, but their various core technologies have their own advantages and disadvantages. This paper aims to describe an alternative to modern decentralized financial networks by introducing the Humanode network. Humanode is a network safeguarded by cryptographically secure bio-authorized nodes. Users will be able to deploy nodes by staking their encrypted biometric data. This approach can potentially lead to the creation of a public, permissionless financial network based on consensus between equal human nodes with algorithm-based emission mechanisms targeting real value growth and proportional emission.
Humanode combines different technological stacks to achieve a decentralized, secure, scalable, efficient, consistent, immutable, and sustainable financial network:
• a bio-authorization module based on cryptographically secure neural networks for the private classification of 3D templates of users' faces
• a private Liveness detection mechanism for identification of real human beings
• a Substrate module as a blockchain layer
• a cost-based fee system
• a Vortex decentralized autonomous organization (DAO) governing system
• a monetary policy and algorithm, Fath, where monetary supply reacts to real value growth and emission is proportional
All of these implemented technologies have nuances that are crucial for the integrity of the network. In this paper we address these details, describing problems that might occur and their possible solutions. The Humanode core acknowledges the power of liveness detection and internal multimodal biometric processing methods that, implemented properly, will tremendously increase resistance against Sybil attacks and overcome the challenges and limitations of modern biometric authentication and identification systems. The main goal of Humanode is to create a stable and just financial network that relies on the existence of human life.
... Heat generated by the facial tissues is unique and has a measurable repeatable pattern. It is more stable than the facial structure [44,45]. ...
Recognizing human beings through biometric properties is a trending innovation. In the past, more attention has been given to biometric recognition because of its importance in security. Out of the current biometric recognition, the fingerprint is classified as a practical method. Methods including steganography and watermarking are being employed to enhance the security of biometric information. Watermarking is a way of implanting data in a carrier file to prevent the copyright/ownership of music, image or video folders. However, the technique involves in concealing information is referred to as steganography. This study outlines the summary of methods used in protecting biometric data present in the fingerprint. Moreover, the advantages, disadvantages and applications of biometrics techniques are explained.
... Heat generated by the facial tissues is unique and has a measurable repeatable pattern. It is more stable than the facial structure (Prokoski et al. 1992), (Hanmandlu et al. 2012). ...
Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.
Cybersecurity threats are serious, costly and challenging because they are various, evolutive and easily spread. This chapter is dedicated to brief these issues, its evolution and the various solutions provided by the researchers; it also surveys the biometric solutions to one of the key issues of cybersecurity which is the intrusion, by providing robust authentication solutions basing on the unique physical and behavioural characteristics of the user.KeywordsCybersecurity solutionsBiometricsThreatsAttacks and authentication
This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognition. Two types of fusion methods are discussed: data fusion and decision fusion. Data fusion produces an illumination-invariant face image by adaptively integrating registered visual and thermal face images. Decision fusion combines matching scores of individual face recognition modules. In the data fusion process, eyeglasses, which block thermal energy, are detected from thermal images and replaced with an eye template. Three fusion-based face recognition techniques are implemented and tested: Data fusion of visual and thermal images (Df), Decision fusion with highest matching score (Fh), and Decision fusion with average matching score (Fa). A commercial face recognition software FaceIt® is used as an individual recognition module. Comparison results show that fusion-based face recognition techniques outperformed individual visual and thermal face recognizers under illumination variations and facial expressions.
We present a comprehensive performance analysis of multiple appearance-based face recognition methodologies, on visible and thermal infrared imagery. We compare algorithms within and between modalities in terms of recognition performance, false alarm rates and requirements to achieve specified performance levels. The effect of illumination conditions on recognition performance is emphasized, as it underlines the relative advantage of radiometrically calibrated thermal imagery for face recognition.
Infrared identification (IRID) is a biometric which offers the
security of fingerprints with the convenience of facial recognition.
Twenty years after being first proposed IRID systems are becoming
marketable. During the intervening time, IR camera technology has
improved significantly and price reductions have been dramatic. Further
improvements in IR camera sensitivity, array size, and variable optics,
as well as further price reductions, are coming, just as interest is
growing in biometrics. Several approaches have been developed, tested,
and evaluated to extract and analyze features from infrared images of
the face and body. IRID has been seen to have significant advantages
over other biometrics in certain applications-not only in the
traditional biometric security markers, but also in communications and
health care
A method for infrared face recognition is proposed based on principal component analysis (PCA) and linear discriminant analysis (LDA). According to the characteristics of infrared face images, a set of normalized infrared face images is gotten by preprocessing. The dimensionality of the image vector is reduced and the global features are extracted. The global features are used to generate a classifier which can minimize the within-class scatter and maximize the between-class scatter. Finally, an infrared face recognition experiment based on the combination of PCA and LDA is performed and the results show the high performance of the proposed method.
Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.
It has been found that facial thermograms vary with ambient temperature, as well as other internal and external conditions, and result in severe decline in the facial recognition rate. To tackle this problem, a skin heat transfer (SHT) model based on thermal physiology is derived in this paper. The proposed model converts the facial thermograms into blood-perfusion data, which is revealed to reduce the within-class scatter of face images. The advantage of the derived blood-perfusion data over the raw thermograms for recognition is analyzed by the normalized reverse cumulative histogram. It is shown that blood-perfusion data are more consistent in representing facial features. The experiments conducted on both same-session and time-lapse data have further demonstrated that (1) the blood-perfusion data are less sensitive to ambient temperature, physiological and psychological conditions if the human bodies are in the steady state; (2) for time-lapse data, the performance with the blood-perfusion data is nearly identical to that of the same-session data, while the recognition rate with the temperature data dramatically decreases in this case. The major contributions of this work are the well-grounded infrared data preprocessing and the corresponding face recognition system.
This paper presents an integrated image fusion and match score fusion of mul- tispectral face images. The fusion of visible and long wave infrared face images is performed using 2-Granular SVM which uses multiple SVMs to learn both the local and global properties of the multispectral face images at dierent granularity levels and resolution. The 2-GSVM performs accurate classification which is sub- sequently used to dynamically compute the weights of visible and infrared images for generating a fused face image. 2D log polar Gabor transform and local binary pattern feature extraction algorithms are applied to the fused face image to extract global and local facial features respectively. The corresponding match scores are fused using Dezert Smarandache theory of fusion which is based on plausible and paradoxical reasoning. The ecacy of the proposed algorithm is validated using the Notre Dame and Equinox databases and is compared with existing statistical, learning, and evidence theory based fusion algorithms.