Thermal-to-visible face recognition using partial least squares

Hu, Shuowen; Choi, Jonghyun; Chan, Alex; Schwartz, William Robson

doi:10.1364/josaa.32.000431

Cited by 85 publications

(48 citation statements)

References 34 publications

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“…Many challenges with this authentication method may arise due to the user conditions: sickness or emotion may significantly influence the perceived figures [111].…”

Section: Thermal Image Recognitionmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir's Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

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“…Many challenges with this authentication method may arise due to the user conditions: sickness or emotion may significantly influence the perceived figures [111].…”

Section: Thermal Image Recognitionmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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“…2.2.1 that the data is used for detecting body parts movement. Recently IR images also enable facial recognition in dark environment by capturing thermal images of human faces to assist the recognition process by comparing details with normal facial images in database (Hu et al 2015). Facial signatures reflecting body conditions due to the fluctuations in skin temperature are being remotely captured by many airports for mass screening of passengers for infection quarantine, e.g.…”

Section: Duis For Human Outputs: Body Temperaturementioning

confidence: 99%

Towards disappearing user interfaces for ubiquitous computing: human enhancement from sixth sense to super senses

Hui

Sherratt

2016

J Ambient Intell Human Comput

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The enhancement of human senses electronically is possible when pervasive computers interact unnoticeably with humans in ubiquitous computing. The design of computer user interfaces towards ''disappearing'' forces the interaction with humans using a content rather than a menu driven approach, thus the emerging requirement for huge number of non-technical users interfacing intuitively with billions of computers in the Internet of Things is met. Learning to use particular applications in ubiquitous computing is either too slow or sometimes impossible so the design of user interfaces must be naturally enough to facilitate intuitive human behaviours. Although humans from different racial, cultural and ethnic backgrounds own the same physiological sensory system, the perception to the same stimuli outside the human bodies can be different. A novel taxonomy for disappearing user interfaces (DUIs) to stimulate human senses and to capture human responses is proposed. Furthermore, applications of DUIs are reviewed. DUIs with sensor and data fusion to simulate the sixth sense is explored. Enhancement of human senses through DUIs and context awareness is discussed as the groundwork enabling smarter wearable devices for interfacing with human emotional memories.

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“…The NVESD dataset, used in [18], contains visible, MWIR, and LWIR face imagery. While the dataset contains imagery from a variety of cameras, we are only interested in the imagery collected using the visible monochrome, MWIR, and LWIR cameras for this work.…”

Section: Nvesd Datasetmentioning

confidence: 99%

“…Cross-spectrum thermal-to-visible face recognition has recently received attention [16][17][18] to provide a face recognition capability for nighttime surveillance, with the objective of matching a thermal probe image acquired in darkness to government databases and watch lists that contain only visible face imagery. For discreet surveillance, being able to effectively utilize low resolution thermal face images acquired at longer ranges is especially important.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality analysis of facial signatures in visible and thermal spectra

Short

Gurram

2015

SPIE Proceedings

Self Cite

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Face images are an important source of information for biometric recognition and intelligence gathering. While face recognition research has made significant progress over the past few decades, recognition of faces at extended ranges is still highly problematic. Recognition of a low-resolution probe face image from a gallery database, typically containing high resolution facial imagery, leads to lowered performance than traditional face recognition techniques. Learning and super-resolution based approaches have been proposed to improve face recognition at extended ranges; however, the resolution threshold for face recognition has not been examined extensively. Establishing a threshold resolution corresponding to the theoretical and empirical limitations of low resolution face recognition will allow algorithm developers to avoid focusing on improving performance where no distinguishable information for identification exists in the acquired signal. This work examines the intrinsic dimensionality of facial signatures and seeks to estimate a lower bound for the size of a face image required for recognition. We estimate a lower bound for face signatures in the visible and thermal spectra by conducting eigenanalysis using principal component analysis (PCA) (i.e., eigenfaces approach). We seek to estimate the intrinsic dimensionality of facial signatures, in terms of reconstruction error, by maximizing the amount of variance retained in the reconstructed dataset while minimizing the number of reconstruction components. Extending on this approach, we also examine the identification error to estimate the dimensionality lower bound for lowresolution to high-resolution (LR-to-HR) face recognition performance. Two multimodal face datasets are used for this study to evaluate the effects of dataset size and diversity on the underlying intrinsic dimensionality: 1) 50-subject NVESD face dataset (containing visible, MWIR, LWIR face imagery) and 2) 119-subject WSRI face dataset (containing visible and MWIR face imagery).

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Thermal-to-visible face recognition using partial least squares

Cited by 85 publications

References 34 publications

Multi-Factor Authentication: A Survey

Multi-Factor Authentication: A Survey

Towards disappearing user interfaces for ubiquitous computing: human enhancement from sixth sense to super senses

Dimensionality analysis of facial signatures in visible and thermal spectra

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