Detecting and Aligning Faces by Image Retrieval
- 1 June 2013
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3460-3467
- https://doi.org/10.1109/cvpr.2013.444
Abstract
Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. The same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection.Keywords
This publication has 15 references indexed in Scilit:
- Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Online domain adaptation of a pre-trained cascade of classifiersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- A Local Bag-of-Features Model for Large-Scale Object RetrievalLecture Notes in Computer Science, 2010
- An HOG-LBP human detector with partial occlusion handlingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- On the Design of Cascades of Boosted Ensembles for Face DetectionInternational Journal of Computer Vision, 2007
- Rapid object detection using a boosted cascade of simple featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Robust Object Detection via Soft CascadePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Fast rotation invariant multi-view face detection based on real adaboostPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Video Google: a text retrieval approach to object matching in videosPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003