  In general, biometry-based control systems may not rely on individual
expected behavior or cooperation to operate appropriately. Instead, such
systems should be aware of malicious procedures for unauthorized access
attempts. Some works available in the literature suggest addressing the problem
through gait recognition approaches. Such methods aim at identifying human
beings through intrinsic perceptible features, despite dressed clothes or
accessories. Although the issue denotes a relatively long-time challenge, most
of the techniques developed to handle the problem present several drawbacks
related to feature extraction and low classification rates, among other issues.
However, deep learning-based approaches recently emerged as a robust set of
tools to deal with virtually any image and computer-vision related problem,
providing paramount results for gait recognition as well. Therefore, this work
provides a surveyed compilation of recent works regarding biometric detection
through gait recognition with a focus on deep learning approaches, emphasizing
their benefits, and exposing their weaknesses. Besides, it also presents
categorized and characterized descriptions of the datasets, approaches, and
architectures employed to tackle associated constraints.
