Miracle: Facial feature extraction using active appearance model
Emotion detection systems that use facial features as input have been around for quite some time. Determining emotions based on facial features sometimes mistake the emotion for another. The Active Appearance Model (AAM), which is a new technology, shows promising facial feature recognition that cou...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2011
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| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/11957 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Emotion detection systems that use facial features as input have been around for quite some time. Determining emotions based on facial features sometimes mistake the emotion for another. The Active Appearance Model (AAM), which is a new technology, shows promising facial feature recognition that could be used to classify emotions based on facial input. Full frontal capture of images was taken as input, and the system identified the region of interest, where the face will be located, which was processed. Once the frames have been fed, it went through a series of image pre-processing. Each image in the AAM requires a total of 68 facial points, which are all relevant. The relevant points were taken from previous system, and other readings, which were used to prove AAMs capabilities over its predecessor, Activate Shape Model (ASM). These relevant facial points will make-up the relevant facial features which will be used for classifying emotions for future systems. Tests were also done, to show other capabilities and limitations of the AAM. The AAM does a better job in face tracking compared to ASM and the use of new features improved the accuracy of emotion classification. |
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