TY - GEN
T1 - Robust face recognition via accurate face alignment and Sparse Representation
AU - Li, Hanxi
AU - Wang, Peng
AU - Shen, Chunhua
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Face recognition
KW - Greedy sparse linear discriminant analysis
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=79951610727&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2010.54
DO - 10.1109/DICTA.2010.54
M3 - Conference contribution
AN - SCOPUS:79951610727
SN - 9780769542713
T3 - Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010
SP - 262
EP - 269
BT - Proceedings - 2010 Digital Image Computing
T2 - International Conference on Digital Image Computing: Techniques and Applications, DICTA 2010
Y2 - 1 December 2010 through 3 December 2010
ER -