  Human identification plays a prominent role in terms of security. In modern
times security is becoming the key term for an individual or a country,
especially for countries which are facing internal or external threats. Gait
analysis is interpreted as the systematic study of the locomotive in humans. It
can be used to extract the exact walking features of individuals. Walking
features depends on biological as well as the physical feature of the object;
hence, it is unique to every individual. In this work, gait features are used
to identify an individual. The steps involve object detection, background
subtraction, silhouettes extraction, skeletonization, and training 3D
Convolution Neural Network on these gait features. The model is trained and
evaluated on the dataset acquired by CASIA B Gait, which consists of 15000
videos of 124 subjects walking pattern captured from 11 different angles
carrying objects such as bag and coat. The proposed method focuses more on the
lower body part to extract features such as the angle between knee and thighs,
hip angle, angle of contact, and many other features. The experimental results
are compared with amongst accuracies of silhouettes as datasets for training
and skeletonized image as training data. The results show that extracting the
information from skeletonized data yields improved accuracy.
