  As a unique and promising biometric, video-based gait recognition has broad
applications. The key step of this methodology is to learn the walking pattern
of individuals, which, however, often suffers challenges to extract the
behavioral feature from a sequence directly. Most existing methods just focus
on either the appearance or the motion pattern. To overcome these limitations,
we propose a sequential convolutional network (SCN) from a novel perspective,
where spatiotemporal features can be learned by a basic convolutional backbone.
In SCN, behavioral information extractors (BIE) are constructed to comprehend
intermediate feature maps in time series through motion templates where the
relationship between frames can be analyzed, thereby distilling the information
of the walking pattern. Furthermore, a multi-frame aggregator in SCN performs
feature integration on a sequence whose length is uncertain, via a mobile 3D
convolutional layer. To demonstrate the effectiveness, experiments have been
conducted on two popular public benchmarks, CASIA-B and OU-MVLP, and our
approach is demonstrated superior performance, comparing with the state-of-art
methods.
