Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, featur...
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| 主要な著者: | , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English English |
| 出版事項: |
2018
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| 主題: | |
| オンライン･アクセス: | http://eprints.unisza.edu.my/1710/1/FH03-FIK-18-13687.jpg http://eprints.unisza.edu.my/1710/2/FH03-FIK-19-23934.pdf http://eprints.unisza.edu.my/1710/ |
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| 要約: | Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics
and features of an individual. Face recognition with high recognition rate is still a challenging task and usually
accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise
and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi
Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently.
Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both
shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and
give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and
gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture. |
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