  Lip motion reflects behavior characteristics of speakers, and thus can be
used as a new kind of biometrics in speaker recognition. In the literature,
lots of works used two-dimensional (2D) lip images to recognize speaker in a
textdependent context. However, 2D lip easily suffers from various face
orientations. To this end, in this work, we present a novel end-to-end 3D lip
motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM)
to recognize speakers in both the text-independent and text-dependent contexts.
A new regional feedback module (RFM) is proposed to obtain attentions in
different lip regions. Besides, prior knowledge of lip motion is investigated
to complement RFM, where landmark-level and frame-level features are merged to
form a better feature representation. Moreover, we present two methods, i.e.,
coordinate transformation and face posture correction to pre-process the LSD-AV
dataset, which contains 68 speakers and 146 sentences per speaker. The
evaluation results on this dataset demonstrate that our proposed 3LMNet is
superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and
outperforms the state-of-the-art using 2D lip image as well as the 3D face. The
code of this work is released at
https://github.com/wutong18/Three-Dimensional-Lip-
Motion-Network-for-Text-Independent-Speaker-Recognition.
