Abstract:
This project develops pose representation for the person's recognition. Although
objects like face are easy to align, but it is more difficult to align the parts of the
human body with big changes. As a result, this project presents a specific visual
model that receives the recognition while keeping the human body in mind. This
project divides the place of human pose into limited groups, in which each sample is
included in special orientation or body perspective. Unlike other project this project,
learn the representation of multi-sectoral infectious convolutional neuronal network
(CNN) for each point. However, unlike previous approaches to creating a neural
network for each area ofthe body, we jointly optimized the network in many areas of
the body. It provides additional flexibility for the network to create predictions based
on some informative body areas. It contradicts with a different training that strictly
applies the correct predictions of each area of the body during testing. We obtain
samples of identifying predictions through linear combination of classified scores,
each of which is formed by the specific representation of the pose. To combine
classifiers, the weight is obtained through a pose estimator, which calculates the
probability of each view.
