Fusion of Face and Voice Information in Person Identity Verification with Class-Imbalanced Dataset

• Main Authors: Hsien-Ting Cheng, 鄭先廷
• Other Authors: Yi-Ping Huang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/72072557861966522849

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 93 === 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.