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Type: Conference paper
Title: Robust face recognition via accurate face alignment and sparse representation
Author: Li, H.
Wang, P.
Shen, C.
Citation: International Conference on Digital Image Computing: Techniques and Applications (DICTA 2010), held in Sydney, Australia 1 - 3 December 2010: pp. 262-269
Publisher: IEEE
Publisher Place: Online
Issue Date: 2010
ISBN: 9780769542713
Conference Name: Digital Image Computing: Techniques and Applications (2010 : Sydney, Australia)
Statement of
Hanxi Li, Peng Wang and Chunhua Shen
Abstract: Due to its potential applications, face recognition has been receiving more and more research attention recently. In this paper, we present a robust real-time facial recognition system. The system comprises three functional components, which are face detection, eye alignment and face recognition, respectively. Within the context of computer vision, there are lots of candidate algorithms to accomplish the above tasks. Having compared the performance of a few state-of-the-art candidates, robust and efficient algorithms are implemented. As for face detection, we have proposed a new approach termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA) that produces better performances than most reported face detectors. Since face misalignment significantly deteriorates the recognition accuracy, we advocate a new cascade framework including two different methods for eye detection and face alignment. We have adopted a recent algorithm termed Sparse Representation-based Classification (SRC) for the face recognition component. Experiments demonstrate that the whole system is highly qualified for efficiency as well as accuracy.
Keywords: consistency constraints
maximum likelihood
multi-projective parameter estimation
multiple homographies
scale invariance
Rights: Copyright IEEE 2010
DOI: 10.1109/DICTA.2010.54
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Computer Science publications

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