Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Gesture passwords: concepts, methods and challenges

Biometrics are a convenient alternative to traditional forms of access control such as passwords and pass-cards since they rely solely on user-specific traits. Unlike alphanumeric passwords, biometrics cannot be given or told to another person, and unlike pass-cards, are always “on-hand.” Perhaps the most well-known biometrics with these properties are: face, speech, iris, and gait. This dissertation proposes a new biometric modality: gestures. A gesture is a short body motion that contains static anatomical information and changing behavioral (dynamic) information. This work considers both full-body gestures such as a large wave of the arms, and hand gestures such as a subtle curl of the fingers and palm. For access control, a specific gesture can be selected as a “password” and used for identification and authentication of a user. If this particular motion were somehow compromised, a user could readily select a new motion as a “password,” effectively changing and renewing the behavioral aspect of the biometric. This thesis describes a novel framework for acquiring, representing, and evaluating gesture passwords for the purpose of general access control. The framework uses depth sensors, such as the Kinect, to record gesture information from which depth maps or pose features are estimated. First, various distance measures, such as the log-euclidean distance between feature covariance matrices and distances based on feature sequence alignment via dynamic time warping, are used to compare two gestures, and train a classifier to either authenticate or identify a user. In authentication, this framework yields an equal error rate on the order of 1-2% for body and hand gestures in non-adversarial scenarios. Next, through a novel decomposition of gestures into posture, build, and dynamic components, the relative importance of each component is studied. The dynamic portion of a gesture is shown to have the largest impact on biometric performance with its removal causing a significant increase in error. In addition, the effects of two types of threats are investigated: one due to self-induced degradations (personal effects and the passage of time) and the other due to spoof attacks. For body gestures, both spoof attacks (with only the dynamic component) and self-induced degradations increase the equal error rate as expected. Further, the benefits of adding additional sensor viewpoints to this modality are empirically evaluated. Finally, a novel framework that leverages deep convolutional neural networks for learning a user-specific “style” representation from a set of known gestures is proposed and compared to a similar representation for gesture recognition. This deep convolutional neural network yields significantly improved performance over prior methods. A byproduct of this work is the creation and release of multiple publicly available, user-centric (as opposed to gesture-centric) datasets based on both body and hand gestures

Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing

Person re-identification is a critical privacy breach in publicly shared healthcare data. We investigate the possibility of a new type of privacy threat on publicly shared privacy insensitive large scale wearable sensing data. In this paper, we investigate user specific biometric signatures in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we propose PhysioGait, a context-aware physiological signal model that consists of a Multi-Modal Siamese Convolutional Neural Network (mmSNN) which learns the spatial and temporal information individually and performs sensor fusion in a Siamese cost with the objective of predicting a person's identity. We evaluated PhysioGait attack model using 4 real-time collected datasets (3-data under IRB #HP-00064387 and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons.Comment: Accepted in IEEE MSN 2022. arXiv admin note: substantial text overlap with arXiv:2106.1190

Biometrics for internet‐of‐things security: A review

The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions