Semi-supervised haartraining of a fast & frugal open source zygomatic smile detector


node:	Semi-supervised haartraining of a fast & frugal open source zygomatic smile detector
template:	12
parent:	Hromada, Daniel
owner:	nanda
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created:	31.08.2010 - 14:25:43

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63532
683817
683643
683657
5122430
5523465

ABSOLUT
KYBERIA

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Five different versions OpenCV-positive XML haarcascades of zygomatic smile-detectors as well as five SMILEsamples from which these detectors were derived had been trained and are presented hereby as a new open source package. Samples have been extended in an incremental learning fashion, exploiting previously trained detector in order to add and label new elements of positive example set. After coupling with already known face detector, overall AUC performance ranges between 77%-90.5% when tested on JAFFE dataset and <1ms per frame speed of smile detection is achieved when tested on webcam-obtained videos.

keywords: zygomatic smile detector; cascade of haar feature classifiers; computer vision; semi-supervised machine learning

download here: Semi-supervised haartraining of a fast & frugal open source zygomatic smile detector

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Introduction
ROBOTO
Facial Expression Recognition
Zygomatic Smile Detection
Method of Viola & Jones

SMILEsmileD

Results
Measuring « smile intensity »
JAFFE-based Evaluation
Evaluation under real-life conditions
Haar feature visualization

Discussion
Theoretical Contribution
Practical Applications

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juchúúúú :)

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Download smileD zygomatic smile detector's OpenCV compatible XML haar cascade

directly from kyberia: http://kyberia.sk/id/5523549/download
or from github: http://github.com/hromi/SMILEsmileD/raw/master/smileD/smiled_05.xml

Functions well for distances less than one meter.

For more recent versions and for related applications, go to http://github.com/hromi/SMILEsmileD

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[1] L. Strathearn, J. Li, P. Fonagy, et P.R. Montague, “What's in a smile? Maternal brain responses to infant facial cues,” Pediatrics, vol. 122, 2008, p. 40.
[2] C. Darwin, P. Ekman, et P. Prodger, The expression of the emotions in man and animals, Oxford University Press, USA, 2002.
[3] M. Akita, K. Marukawa, et S. Tanaka, “Imaging apparatus and display control method,” 2010.
[4] J.R. Movellan, F. Tanaka, I.R. Fasel, C. Taylor, P. Ruvolo, et M. Eckhardt, “The RUBI project: a progress report,” Proceedings of the ACM/IEEE international conference on Human-robot interaction, 2007, p. 339.
[5] G. Bradski et A. Kaehler, Learning OpenCV, O'Reilly Media, Inc., 2008.
[6] P. Viola et M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple,” Proc. IEEE CVPR 2001.
[7] O. Deniz, M. Castrillon, J. Lorenzo, L. Anton, et G. Bueno, “Smile Detection for User Interfaces,” Advances in Visual Computing, p. 602–611.
[8] J. Whitehill, M. Bartlett, G. Littlewort, I. Fasel, et J. Movellan, “Developing a practical smile detector,” Submitted to PAMI, vol. 3, 2007, p. 5.
[9] P. Ekman et W.V. Friesen, Pictures of facial affect, Palo Alto, CA: Consulting Psychologists Press, 1976.
[10] T. Kanade, Y. Tian, et J.F. Cohn, “Comprehensive database for facial expression analysis,” fg, 2000, p. 46.
[11] G.B. Huang, M. Ramesh, T. Berg, et E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition” University of Massachusetts, Amherst, Technical Report, vol. 57, 2007, p. 07–49.
[12] J. Whitehill, G. Littlewort, I. Fasel, M. Bartlett, et J. Movellan, “Toward Practical Smile Detection,” IEEE transactions on pattern analysis and machine intelligence, 2009, p. 2106–2111.
[13] L. Da Vinci et J.P. Richter, The notebooks of Leonardo da Vinci, Dover Publications, 1970.
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Great amount of work is being done in the domain of facial expression (FE) recognition. Of particular interest is a FE being at the very base of mother-baby interaction [1], a FE interpreted unequivocally in all human cultures [2] - smile. Maybe because of these reasons, maybe because of some others, smile detection is already of certain interest for computer vision (CV) community – be it for camera's smile shutter [3] or in order to study robot2children interaction [4].

Nonetheless, a publicly available i.e. open source, smile detector is missing. This is somewhat stunning, especially given the fact that “smile” can be conceived as a “blocky” object [5] upon which a machine learning technique based on training of cascades of boosted haar-feature classifiers [6] can be applied, and that the tools for performing such a training are already publicly available as part of an OpenCV[5] project. Verily, with exceptions of detectors described in [7][8] which have not been publicly released, we did not find any reference to haarcascade-based smile detector in the literature. We aim to address this issue by making publicly available the initial results of our attempts to construct sufficiently descriptive SMILing Multisource Incremental-Learning Extensible Sample (SMILEs) and five smile detectors (smileD) generated from this sample.

From more general perspective, our aim was to study whether one can use already generated classifiers in order to facilitate such a semi-supervised extension of initial sample that a more accurate classifier can be subsequently trained.

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The aim of SMILEs project is to facilitate and accelerate the construction of smile detectors to anyone willing to do so. Since it is the OpenCV library which dominates the computer vision community, SMILEs package is adapted upon the needs of OpenCV in a sens that it contains 1) negative examples directory 2) positive examples directory 3) negatives.idx - list of files in negative examples directory 4) positives.idx - list of files in positives with associated information containing the coordinates of region of interest (ROI), i.e. the coordinates of the region within which smile can be located.

SMILEs is considered “Multisource” because it originates as an amalgam of already existing datasets like LFW and Genki both of which are, themselves, collections of images downloaded from the Internet. Images from POFA [9] of Cohn-Kanade [10] datasets were not included into SMILEs since restricted access to these datasets is in contradiction with an open source approach1 of SMILEs project.

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SMILEs are “Incremental-Learning Extensible” in a sense that they allow us to train new versions of smile detectors which are subsequently applied upon new image datasets in order to facilitate (or even fully automatize) the labeling of new images, and hence extending an original SMILEs with new images. Simply stated, SMILEs allow us train smileD which helps us to extend SMILEs etc.

Since training of haar cascades is an exhaustive threshrold-finding process demanding not negligible amount of time and computational resources, 5 pregenerated OpenCV-compatible XML smileD haarcascades were trained by opencv-haartraining application and are included with SMILEs in our OpenSource SMILEsmileD package, so that anybody interested could implement our smile detector in copy&use fashion.

axone image processing (openCV a podobne)
axone umela inteligencia
axone moje prace