Journal ArticleDOI
Face Recognition in Poor-Quality Video: Evidence From Security Surveillance:
TLDR
In this article, the authors examined the ability of subjects to identify target people captured by a commercially available video security device and found that subjects who were personally familiar with the targets performed very well at identifying them, but subjects unfamiliar with the target performed very poorly.Abstract:
Security surveillance systems often produce poor-quality video, and this may be problematic in gathering forensic evidence. We examined the ability of subjects to identify target people captured by a commercially available video security device. In Experiment 1, sub- jects personally familiar with the targets performed very well at iden- tifying them, but subjects unfamiliar with the targets performed very poorly. Police officers with experience in forensic identification per- formed as poorly as other subjects unfamiliar with the targets. In Experiment 2, we asked how familiar subjects can perform so well. Using the same video device, we edited clips to obscure the head, body, or gait of the targets. Obscuring body or gait produced a small decrement in recognition performance. Obscuring the targets' heads had a dramatic effect on subjects' ability to recognize the targets. These results imply that subjects recognized the targets' faces, even in these poor-quality images.read more
Citations
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Journal ArticleDOI
Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About
TL;DR: Findings from experimental studies of face recognition by humans provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities.
Journal ArticleDOI
Recognition of unfamiliar faces
TL;DR: The relationships between different parts of the face (its 'configuration') are as important to the impression created of an upright face as the local features themselves, suggesting further constraints on the representations derived from faces.
Journal ArticleDOI
Verification of face identities from images captured on video.
Journal ArticleDOI
Describable Visual Attributes for Face Verification and Image Search
TL;DR: It is shown how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images, which can then automatically label new images.
Journal ArticleDOI
Recognizing moving faces: a psychological and neural synthesis.
TL;DR: A recently proposed distributed neural system for face perception, with minor modifications, can accommodate the psychological findings with moving faces.
References
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Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Journal ArticleDOI
Understanding face recognition
Vicki Bruce,Andrew W. Young +1 more
TL;DR: A functional model is proposed in which structural encoding processes provide descriptions suitable for the analysis of facial speech, for analysis of expression and for face recognition units, and it is proposed that the cognitive system plays an active role in deciding whether or not the initial match is sufficiently close to indicate true recognition.
Journal ArticleDOI
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
Proceedings ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.