Editing Affective computing (section)

====Challenges in facial detection====
As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost every kind of AI-based detection (speech recognition, face recognition, affect recognition), the accuracy of modeling and tracking has been an issue. As hardware evolves, as more data are collected and as new discoveries are made and new practices introduced, this lack of accuracy fades, leaving behind noise issues. However, methods for noise removal exist including neighborhood averaging, [[Gaussian blur|linear Gaussian smoothing]], median filtering,<ref>{{cite web|url=http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OWENS/LECT5/node3.html|title=Spatial domain methods}}</ref> or newer methods such as the Bacterial Foraging Optimization Algorithm.<ref>Clever Algorithms. [http://www.cleveralgorithms.com/nature-inspired/swarm/bfoa.html "Bacterial Foraging Optimization Algorithm – Swarm Algorithms – Clever Algorithms"] {{Webarchive|url=https://web.archive.org/web/20190612144816/http://www.cleveralgorithms.com/nature-inspired/swarm/bfoa.html |date=2019-06-12 }}. Clever Algorithms. Retrieved 21 March 2011.</ref><ref>[http://www.softcomputing.net/bfoa-chapter.pdf "Soft Computing"]. Soft Computing. Retrieved 18 March 2011.</ref>

Other challenges include
* The fact that posed expressions, as used by most subjects of the various studies, are not natural, and therefore algorithms trained on these may not apply to natural expressions.
* The lack of rotational movement freedom. Affect detection works very well with frontal use, but upon rotating the head more than 20 degrees, "there've been problems".<ref>Williams, Mark. [http://www.technologyreview.com/Infotech/18796/?a=f "Better Face-Recognition Software – Technology Review"] {{Webarchive|url=https://web.archive.org/web/20110608023222/http://www.technologyreview.com/Infotech/18796/?a=f |date=2011-06-08 }}. Technology Review: The Authority on the Future of Technology. Retrieved 21 March 2011.</ref>
* Facial expressions do not always correspond to an underlying emotion that matches them (e.g. they can be posed or faked, or a person can feel emotions but maintain a "poker face").
* FACS did not include dynamics, while dynamics can help disambiguate (e.g. smiles of genuine happiness tend to have different dynamics than "try to look happy" smiles.)
* The FACS combinations do not correspond in a 1:1 way with the emotions that the psychologists originally proposed  (note that this lack of a 1:1 mapping also occurs in speech recognition with homophones and homonyms and many other sources of ambiguity, and may be mitigated by bringing in other channels of information).
* Accuracy of recognition is improved by adding context; however, adding context and other modalities increases computational cost and complexity