  Recent advances in deep learning have heightened interest among researchers
in the field of visual speech recognition (VSR). Currently, most existing
methods equate VSR with automatic lip reading, which attempts to recognise
speech by analysing lip motion. However, human experience and psychological
studies suggest that we do not always fix our gaze at each other's lips during
a face-to-face conversation, but rather scan the whole face repetitively. This
inspires us to revisit a fundamental yet somehow overlooked problem: can VSR
models benefit from reading extraoral facial regions, i.e. beyond the lips? In
this paper, we perform a comprehensive study to evaluate the effects of
different facial regions with state-of-the-art VSR models, including the mouth,
the whole face, the upper face, and even the cheeks. Experiments are conducted
on both word-level and sentence-level benchmarks with different
characteristics. We find that despite the complex variations of the data,
incorporating information from extraoral facial regions, even the upper face,
consistently benefits VSR performance. Furthermore, we introduce a simple yet
effective method based on Cutout to learn more discriminative features for
face-based VSR, hoping to maximise the utility of information encoded in
different facial regions. Our experiments show obvious improvements over
existing state-of-the-art methods that use only the lip region as inputs, a
result we believe would probably provide the VSR community with some new and
exciting insights.
