NDLI: Dynamic cascades with bidirectional bootstrapping for spontaneous facial action unit detection

Content Provider	IEEE Xplore Digital Library
Author	Yunfeng Zhu De la Torre, F. Cohn, J.F. Yu-Jin Zhang
Copyright Year	2009
Description	Author affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084 (Yunfeng Zhu; Yu-Jin Zhang) \|\| Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 (De la Torre, F.; Cohn, J.F.)
Abstract	A relatively unexplored problem in facial expression analysis is how to select the positive and negative samples with which to train classifiers for expression recognition. Typically, for each action unit (AU) or other expression, the peak frames are selected as positive class and the negative samples are selected from other AUs. This approach suffers from at least two drawbacks. One, because many state of the art classifiers, such as Support Vector Machines (SVMs), fail to scale well with increases in the number of training samples (e.g. for the worse case in SVM), it may be infeasible to use all potential training data. Two, it often is unclear how best to choose the positive and negative samples. If we only label the peaks as positive samples, a large imbalance will result between positive and negative samples, especially for infrequent AU. On the other hand, if all frames from onset to offset are labeled as positive, many may differ minimally or not at all from the negative class. Frames near onsets and offsets often differ little from those that precede them. In this paper, we propose Dynamic Cascades with Bidirectional Bootstrapping (DCBB) to address these issues. DCBB optimally selects positive and negative class samples in training sets. In experimental evaluations in non-posed video from the RU-FACS Database, DCBB yielded improved performance for action unit recognition relative to alternative approaches.
Starting Page	1
Ending Page	8
File Size	552803
Page Count	8
File Format	PDF
ISBN	9781424448005
DOI	10.1109/ACII.2009.5349603
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Gold Databases Face recognition Support vector machine classification Psychology Training data Facial muscles Face detection Robots
Content Type	Text
Resource Type	Article

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