- Title
- A face-house paradigm for architectural scene analysis
- Creator
- Chalup, Stephan K.; Hong, Kenny; Ostwald, Michael J.
- Relation
- 5th International Conference on Soft Computing As Transdisciplinary Science and Technology (CSTST 2008). The Fifth International Conference on Soft Computing As Transdisciplinary Science and Technology: Proceedings (Paris, France 27-31 October, 2008) p. 397-403
- Publisher Link
- http://dx.doi.org/10.1145/1456223.1456304
- Publisher
- Association for Computing Machinery
- Resource Type
- conference paper
- Date
- 2008
- Description
- This interdisciplinary study proposes a method for architectural design analysis of house façades which is based on face detection and facial expression classification. The hypothesis is that abstract face expression features can occur in the architectural design of house façades and will potentially trigger emotional responses of observers. The approach used statistical learning with support vector machines for classification. In the computer experiments the system was trained using a specifically composed image data base consisting of human faces and smileys. Afterwards it was applied to a series of test images of human facial expressions and house façades. The experiments show how facial expression pattern associated with emotional states such as surprise, fear, happiness, sadness, anger, disgust, contempt or neutral could be recognised in both image data sets.
- Subject
- affective computing; architecture; face recognition; image processing; support vector machines
- Identifier
- http://hdl.handle.net/1959.13/802649
- Identifier
- uon:6155
- Identifier
- ISBN:9781605580463
- Rights
- © ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in The Fifth International Conference on Soft Computing As Transdisciplinary Science and Technology: Proceedings (Paris, France 27-31 October, 2008) p. 397-403 http://dx.doi.org/10.1145/1456223.1456304
- Language
- eng
- Full Text
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