Gait recognition in real environment using gait energy image generated by Mask R-CNN

Currently, biometric authentication is being actively researched as a personal authentication technology for security, and gait recognition that uses Convolutional Neural Networks (CNN) for recognizing human walking is one of them. When creating a Gait Energy Image (GEI) using background subtraction, noises such as shadows and illumination fluctuations often hinder the accuracy of the method. In this paper, GEI with noise removal is created by using Mask R-CNN, and CNN is strengthened by applying Batch Normalization to CNN used for gait recognition. The effectiveness of this method was confirmed by conducting experiments on two types of gaits, one with no bag and one with a bag.