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Challenge based visual speech recognition using deep learning

机译:使用深度学习的基于挑战的视觉语音识别

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We present a novel approach to liveness verification based on visual speech recognition within a challenge-based framework which has the potential to be used on mobile devices to prevent replay or spoof attacks during Face-based liveness verification. The system uses model visual speech recognition and determines liveness based on the Levenshtein Distance between a randomly generated challenge phrase and the hypothesis utterances from the visual speech recognizer. A Deep learning-based approach to visual speech recognition is used to improve upon the state of the art for the use of visual speech recognition for liveness verification.
机译:我们提出了一种新颖的方法,用于在基于挑战的框架内基于视觉语音识别进行活动性验证,该方法有可能在移动设备上使用,以防止在基于面部的活动性验证期间发生重放或欺骗攻击。该系统使用模型视觉语音识别,并根据随机生成的质询短语与视觉语音识别器的假设话语之间的Levenshtein距离来确定活动性。使用基于深度学习的视觉语音识别方法来改进现有技术,以将视觉语音识别用于活动性验证。

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