Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
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ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
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Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Face recognition using the POEM descriptor
Pattern Recognition
Real-Time face detection using integral histogram of multi-scale local binary patterns
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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Expert Systems with Applications: An International Journal
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ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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Multiple instance classification: Review, taxonomy and comparative study
Artificial Intelligence
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Signal Processing
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We propose a scalable face matching algorithm capable of dealing with faces subject to several concurrent and uncontrolled factors, such as variations in pose, expression, illumination, resolution, as well as scale and misalignment problems. Each face is described in terms of multi-region probabilistic histograms of visual words, followed by a normalised distance calculation between the histograms of two faces. We also propose a fast histogram approximation method which dramatically reduces the computational burden with minimal impact on discrimination performance. Experiments on the "Labeled Faces in the Wild" dataset (unconstrained environments) as well as FERET (controlled variations) show that the proposed algorithm obtains performance on par with a more complex method and displays a clear advantage over predecessor systems. Furthermore, the use of multiple regions (as opposed to a single overall region) improves accuracy in most cases, especially when dealing with illumination changes and very low resolution images. The experiments also show that normalised distances can noticeably improve robustness by partially counteracting the effects of image variations.