A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Improved Boosting Algorithms Using Confidence-rated Predictions
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ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
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Neural network-based face detection
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International Journal of Computer Vision
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SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Face detection based on multi-block LBP representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Learning multi-scale block local binary patterns for face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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This paper proposes a novel face detection method using local gradient patterns (LGP), in which each bit of the LGP is assigned the value one if the neighboring gradient of a given pixel is greater than the average of eight neighboring gradients, and 0 otherwise. LGP representation is insensitive to global intensity variations like the other representations such as local binary patterns (LBP) and modified census transform (MCT), and to local intensity variations along the edge components. We show that LGP has a higher discriminant power than LBP in both the difference between face histogram and non-face histogram and the detection error based on the face/face distance and face/non-face distance. We also reduce the false positive detection error greatly by accumulating evidences from multi-scale detection results with negligible extra computation time. In experiments using the MIT+CMU and FDDB databases, the proposed LGP-based face detection followed by evidence accumulation method provides a face detection rate that is 5-27% better than those of existing methods, and reduces the number of false positives greatly.