Comparison Between Face and Gait Human Recognition Using Enhanced Convolutional Neural Network

Abstract

Identifying people at distance is an important task in daily life Because of the increase in terrorism. Biometrics is a better solution to overcome personal identity problems, and this applies to soft biometrics also. Soft biometric are features that can be extracted remotely and do not require cooperation with people. This paper introduces a comparison between human face recognition and human gait recognition using soft biometric features. Nine face attributes and nine gait attributes are taken from a dataset built by researchers. The constructed dataset is composed from (66) videos for (33) persons. Features are extracted using Haar and MediaPipe methods. The extracted features are classified using enhanced convolutional neural network. This work achieves an accuracy of 95.832% in human face recognition and an accuracy of 89.583% in human gait recognition. From the above results it turns out that the proposed method achieved promising results with regard to Recognize people remotely