Recent advances in visual and infrared face recognition - a review

By Seong-Gon Kong, Jingu Heo, Besma Roui-Abidi, J. Paik, Mongi A. Abidi. Computer Vision and Image Understanding, 2005

674 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9c5c30e396b307010c374ef3730e84f6cf763205

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Citations in the corpus

Visible and Infrared Image Fusion Using Deep Learning (2023, background): “plications, such as object tracking [7], [8], [9], [10], [11], object detection [12], [13], [14], [15], and biometric recognition [16], [17].”
A survey of skin-color modeling and detection methods (2007, background): “Many of the problems encountered in visual spectrum can be overcome by using non-visual spectrum such as infrared (IR) [5] [6] and spectral imaging [7] [8] [9].”
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning (2005, background): “…is highly complex and understanding it involves a substantial commonality with research in machine analysis and understanding of human action and behavior; for example, face and facial expression recognition [84], [104], tracking and human motion analysis [53], [148], and gesture recognition [106].”
A Survey of Face Recognition Techniques (2009, background): “A comprehensive review of recent advances in face recognition from infra-red imagery may be found in [268].”
A survey of video datasets for human action and activity recognition (2013, background): “Finally, the pose or gesture recognition is dedicated to actions performed by a specific part of the human body, such as hands, arms or upper body part [48,98,167].”
Illumination Invariant Face Recognition Using Near-Infrared Images (2007, background): “Thermal or far infrared imagery has been used for face recognition (cf. a survey paper [36]).”
A review of biometric technology along with trends and prospects (2014, background): “Few studies [87, 88] claimed to achieve better accuracy in terms of face detection, localization and segmentation against the facial images acquired in visible light.”
Respectful cameras: detecting visual markers in real-time to address privacy concerns (2007, background): “Unfortunately, these methods have difficulty detecting and tracking in real-time while being robust to partial occlusions, arbitrary pose, and changing lighting conditions [18], [19].”
Frankenstein: Learning Deep Face Representations Using Small Data (2016, unknown): “infrared imaging is ideal for low-light nighttime and covert face recognition applications [24], but it is not possible to”
Understanding OSN-based facial disclosure against face authentication systems (2014, background): “The variation of lighting conditions mainly includes illumination and low lighting [9, 43, 20].”
SCface – surveillance cameras face database (2011, background): “Many papers are written and many real-world systems are being developed and distributed [1, 12, 19, 22].”