  People identification in video based on the way they walk (i.e. gait) is a
relevant task in computer vision using a non-invasive approach. Standard and
current approaches typically derive gait signatures from sequences of binary
energy maps of subjects extracted from images, but this process introduces a
large amount of non-stationary noise, thus, conditioning their efficacy. In
contrast, in this paper we focus on the raw pixels, or simple functions derived
from them, letting advanced learning techniques to extract relevant features.
Therefore, we present a comparative study of different Convolutional Neural
Network (CNN) architectures by using three different modalities (i.e. gray
pixels, optical flow channels and depth maps) on two widely-adopted and
challenging datasets: TUM-GAID and CASIA-B. In addition, we perform a
comparative study between different early and late fusion methods used to
combine the information obtained from each kind of modalities. Our experimental
results suggest that (i) the raw pixel values represent a competitive input
modality, compared to the traditional state-of-the-art silhouette-based
features (e.g. GEI), since equivalent or better results are obtained; (ii) the
fusion of the raw pixel information with information from optical flow and
depth maps allows to obtain state-of-the-art results on the gait recognition
task with an image resolution several times smaller than the previously
reported results; and, (iii) the selection and the design of the CNN
architecture are critical points that can make a difference between
state-of-the-art results or poor ones.
