Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
A feature construction method for general object recognition
Pattern Recognition
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Viola-Jones approach to object detection is by far the most widely used object detection technique because of speed of detection in images with clutter. SVM-based object detection techniques have the disadvantage of slow detection speeds because of exhaustive window search. Appearance-based detection techniques do not generalize well in the presence of pose variations. In this paper, we propose a feature-based technique which classifies salient-points as belonging to object or background classes and performs object detection based on classified key points. Since keypoints are sparse, the technique is very fast. The use of SIFT descriptor provides invariance to scale and pose changes.