Automated annotation of human faces in family albums
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Automatically converting photographic series into video
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient propagation for face annotation in family albums
Proceedings of the 12th annual ACM international conference on Multimedia
Imlooking: image-based face retrieval in online dating profile search
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Face detection and facial feature localization without considering the appearance of image context
Image and Vision Computing
Face Hallucination: Theory and Practice
International Journal of Computer Vision
A view-based statistical system for multi-view face detection and pose estimation
Image and Vision Computing
Automatic human face counting in digital color images
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
Reliable and fast eye detection
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
A data mining approach to face detection
Pattern Recognition
Directional Hartley transform and content based image retrieval
Signal Processing
IEEE Transactions on Multimedia
Absolute contrasts in face detection with adaboost cascade
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
FEA-Accu cascade for face detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Curve mapping based illumination adjustment for face detection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
How to train a classifier based on the huge face database?
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Face appeal model based on statistics
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Perception based lighting balance for face detection
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Face detection based on the manifold
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Novel face detection method based on gabor features
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Perception-Based lighting adjustment of image sequences
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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Automatic human face detection from images in surveillance and biometric applications is a challenging task due to variations in image background, view, illumination, articulation, and facial expression. We propose a novel three-step face detection approach to addressing this problem. The approach adopts a simple-to-complex strategy. First, a linear-filtering algorithm is applied to enhance detection performance by removing most nonface-like candidates rapidly. Second, a boosting chain algorithm is adopted to combine the boosting classifiers into a hierarchical "chain" structure. By utilizing the inter-layer discriminative information, this algorithm reveals a higher efficiency than traditional approaches. Last, a postfiltering algorithm, consisting of image preprocessing; support vector machine-filter and color-filter, is applied to refine the final prediction. As only a few candidate windows remain in the final stage, this algorithm greatly improves detection accuracy with small computation cost. Compared with conventional approaches, this three-step approach is shown to be more effective and capable of handling more pose variations. Moreover, together with a two-level hierarchy in-plane pose estimator, a rapid multiview face detector is built. Experimental results demonstrate a significant performance improvement for the proposed approach over others.