Fusion of Face and Voice Information in Person Identity Verification with Class-Imbalanced Dataset
Date Issued
2005
Date
2005
Author(s)
Cheng, Hsien-Ting
DOI
en-US
Abstract
Based on the idea of more information brings better performance, in this thesis we presents a confidence-level fusion method to combine face and voice information in biometric person identity verification. In systematic aspect, we develop an on-line verification system with light-weight enrollment process, fraud precaution mechanism and an easy-to-use verification interface. While in algorithmic point of view, state-of-the art techniques are used to build the face and voice experts. More-over, a multi-face/single-sentence strategy is proposed to utilize all the available in-formation to reduce the cost of miss-detection and miss-registration of face, and support vector machine (SVM) is employed as the binary fusion classifier.
In addition to individual experts and the fusion work, another important issue proposed in this thesis is learning from a class-imbalanced dataset. To train a good classifier, most of the time we use as many training data as possible. However in lots of fields involving classification jobs, training data is highly imbalanced distributed from class to class, ordinary classification algorithms will favor to the class which has more training samples. In the field of identity verification we are the first one that discover such important issue and try to handle it. Different level approaches are studied and implemented to reduce the influence of imbalanced dataset and lead to better performance.
In addition to individual experts and the fusion work, another important issue proposed in this thesis is learning from a class-imbalanced dataset. To train a good classifier, most of the time we use as many training data as possible. However in lots of fields involving classification jobs, training data is highly imbalanced distributed from class to class, ordinary classification algorithms will favor to the class which has more training samples. In the field of identity verification we are the first one that discover such important issue and try to handle it. Different level approaches are studied and implemented to reduce the influence of imbalanced dataset and lead to better performance.
Subjects
身份確認
人臉確認
語者確認
多模式整合
支持分類器
不平衡資料集
person identity verification
face verification
speaker verification
multimodal fusion
SVM
class-imbalanced dataset
Type
thesis
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