End-to-End Multi-View Lipreading

Stavros Petridis, Yujiang Wang, Zuwei Li, Maja Pantic

    Research output: Contribution to conferencePaperAcademicpeer-review

    15 Citations (Scopus)

    Abstract

    Non-frontal lip views contain useful information which can be used to enhance the performance of frontal view lipreading. However, the vast majority of recent lipreading works, including the deep learning approaches which significantly outperform traditional approaches, have focused on frontal mouth images. As a consequence, research on joint learning of visual features and speech classification from multiple views is limited. In this work, we present an end-to-end multi-view lipreading system based on Bidirectional Long-Short Memory (BLSTM) networks. To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and performs visual speech classification from multiple views and also achieves state-of-the-art performance. The model consists of multiple identical streams, one for each view, which extract features directly from different poses of mouth images. The temporal dynamics in each stream/view are modelled by a BLSTM and the fusion of multiple streams/views takes place via another BLSTM. An absolute average improvement of 3% and 3.8% over the frontal view performance is reported on the OuluVS2 database when the best two (frontal and profile) and three views (frontal, profile, 45) are combined, respectively. The best three-view model results in a 10.5% absolute improvement over the current multi-view state-of-the-art performance on OuluVS2, without using external databases for training, achieving a maximum classification accuracy of 96.9%.
    Original languageEnglish
    Number of pages14
    Publication statusPublished - 1 Sep 2017
    EventBritish Machine Vision Conference, BMVC 2017 - Imperial College London, London, United Kingdom
    Duration: 4 Sep 20177 Sep 2017

    Conference

    ConferenceBritish Machine Vision Conference, BMVC 2017
    Abbreviated titleBMVC
    CountryUnited Kingdom
    CityLondon
    Period4/09/177/09/17

    Keywords

    • cs.CV

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