@inproceedings{3f55a00154c8411e9278077f2f8c0d63,
title = "Face detection: Histogram of oriented gradients and bag of feature method",
abstract = "Face detection has been one of the most studied topics in computer vision literature; so many algorithms have been developed with different approaches to overcome some detection problems such as occlusion, illumination condition, scale, among others. Histograms of Oriented Gradients are an effective descriptor for object recognition and detection. These descriptors are powerful to detect faces with occlusions, pose and illumination changes because they are extracted in a regular grid. We calculate and vector quantizes into different codewords each descriptor and then we construct histograms of this codeword distribution that represent the face image. Finally, a set of experiments are presented to analyze the performance of this method.",
keywords = "Bag of features, Codeword, Descriptor, Face Detection, Histogram of Oriented Gradients",
author = "Cerna, {L. R.} and G. C{\'a}mara-Ch{\'a}vez and D. Menotti",
note = "Publisher Copyright: {\textcopyright} 2013 CSREA Press. All rights reserved.; 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013 ; Conference date: 22-07-2013 Through 25-07-2013",
year = "2013",
language = "Ingl{\'e}s",
series = "Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013",
publisher = "CSREA Press",
pages = "657--661",
editor = "Arabnia, {Hamid R.} and Leonidas Deligiannidis and Joan Lu and Tinetti, {Fernando G.} and Jane You and George Jandieri and Gerald Schaefer and Solo, {Ashu M. G.} and Vladimir Volkov",
booktitle = "Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013",
}