Face recognition using AAM and global shape features

A new technique for face recognition is proposed, which uses active appearance model (AAM) to extract facial feature points and uses global shape features to recognize face. To enhance performance of AAM, we use Adaboost to locate positions of eyes. After extraction of facial feature points, we use...

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Bibliographic Details
Main Authors: Jia Hong Chen, Han Pang Huang
Format: Conference Proceeding
Language:English
Subjects:
AAM
Active appearance model
Active shape model
Eyes
Face detection
Face recognition
Facial features
Mechanical engineering
Principal component analysis
Shape control
Support vector machines
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Summary:A new technique for face recognition is proposed, which uses active appearance model (AAM) to extract facial feature points and uses global shape features to recognize face. To enhance performance of AAM, we use Adaboost to locate positions of eyes. After extraction of facial feature points, we use any two points of global shape features and compute the distance of two points as a descriptor to construct the whole descriptors of a face. To reduce computation, we use principle component analysis (PCA) to reduce the dimensions of descriptors. Moreover, either support vector machines (SVMs) or k-nearest-neighbor (K-NN) is used to increase recognition rates. In contrast with the conventional recognition algorithm such as Eigenfaces, our method performs better under varying illumination because we use global shape features rather than gray scale pixel values. At last, we demonstrate our approach by experiments.
DOI:10.1109/ROBIO.2009.4913106