Facial Feature Extraction Using Embedded Hidden Markov Model for Face Recognition
Date Issued
2007
Date
2007
Author(s)
Wang, Yun-Wen
DOI
en-US
Abstract
We propose an algorithm for extracting facial features robustly from images for face recognition even under large pose variation. Rectangular facial features are retrieved via the by-products of an embedded Hidden Markov Model (HMM) which decodes an observed face image into a state sequence. While an HMM is able to segment images into features at a fixed pose, multiple HMMs are trained for each individual to robustly extract features under large pose variation. Using the extracted features of each individual, appearance models based on subspaces are constructed for face identification and verification. Then Adaboost is used for feature combination while each weak classifier compared the distance metric of one facial feature. The effectiveness of the proposed approach is validated through empirical studies against numerous methods using the CMU PIE, ORL and our lab’s database. Our experiments demonstrate that the proposed approach is able to extract facial features robustly, thereby rendering superior results in identification and superior performance in verification under large pose variation.
Subjects
人臉身份比對
人臉身份確認
基於特徵的人臉辨識
人臉特徵合併
Face Identification
Face Verification
Component-based Face Recognition
Facial Feature Combination
Type
thesis
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