In the Pursuit of Effective Affective Computing: The Relationship Between Features and Registration

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
S W ChewSridha Sridharan

Abstract

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearan...Continue Reading

References

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Jan 17, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Thomas SerreTomaso Poggio
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Citations

Sep 12, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Evangelos SariyanidiAndrea Cavallaro
Jan 7, 2015·IEEE Transactions on Cybernetics·Jing LiXuelong Li
Oct 16, 2015·Pattern Recognition Letters·Jeffrey M GirardFernando De la Torre
Nov 8, 2014·Image and Vision Computing·Jeffrey M GirardDean P Rosenwald
Jan 24, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence· Wen-Sheng ChuJeffrey F Cohn

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