Handling session mismatch by fusion-based co-training: An empirical study using face and speech multimodal biometrics

N. Poh, J. Kittler, A. Rattani
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引用次数: 8

Abstract

Semi-supervised learning has been shown to be a viable training strategy for handling the mismatch between training and test samples. For multimodal biometric systems, classical semi-supervised learning strategies such as self-training and co-training may not have fully exploited the advantage of a multimodal fusion, notably due to the fusion module. For this reason, we explore a novel semi-supervised training strategy known as fusion-based co-training that generalizes the classical co-training such that it can use a trainable fusion classifier. Our experiments on the BANCA face and speech database show that this proposed strategy is a viable approach. In addition, we also address the resolved issue of how to select the decision threshold for adaptation. In particular, we find that a strong classifier, including a multimodal system, may benefit better from a more relaxed threshold whereas a weak classifier may benefit better from a more stringent one.
基于融合的会话不匹配处理:基于人脸和语音多模态生物识别的实证研究
半监督学习已被证明是处理训练样本与测试样本不匹配的一种可行的训练策略。对于多模态生物识别系统,经典的半监督学习策略,如自我训练和共同训练,可能没有充分利用多模态融合的优势,特别是由于融合模块。出于这个原因,我们探索了一种新的半监督训练策略,称为基于融合的协同训练,它对经典的协同训练进行了推广,从而可以使用可训练的融合分类器。我们在BANCA人脸和语音数据库上的实验表明,该策略是一种可行的方法。此外,我们还解决了如何选择适应的决策阈值的解决问题。特别是,我们发现一个强分类器,包括一个多模态系统,可能会从一个更宽松的阈值中受益更好,而一个弱分类器可能会从一个更严格的阈值中受益更好。
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