Machine Learning based Gait Recognition for Human Authentication

This study presents an approach for human authentication using gait recognition. Gait recognition is a biometric authentication method based on the unique way an individual walk. Human gait is behavioral biometrics that involves both the appearance of the body and the dynamics of motion for human walking. Intra-class variation, which involves different carrying condition, variation in clothes and normal walking with different variations in view angles that interfere affect the efficiency in recognition. The suggested method extracts body position information from the input video sequence using a feature extraction algorithm based on the Open Pose Algorithm. This information is then used to train a Decision Tree classifier to distinguish between different individuals as single person or multiple person. The suggested system is tested on a gait identification dataset, and its performance is measured in terms of accuracy, specificity, and sensitivity. The findings show that the proposed method can successfully identify a person from their gait. The accuracy of the proposed system was found to be significantly higher than that of the existing systems. In terms of computational complexity as well as memory consumption, the suggested system is also superior.