Model-based and model-free deep features fusion for high performed human gait recognition.

Area of Science:

• Computer Science
• Biometrics
• Artificial Intelligence

Background:

• Increased demand for non-contact biometric systems post-COVID-19 pandemic.
• Need for secure and accurate human authentication methods.
• Limitations of existing biometric models in real-world scenarios.

Purpose of the Study:

• To develop a novel deep convolutional neural network (CNN) for non-contact human authentication.
• To enhance recognition accuracy and efficiency using gait and pose analysis.
• To fuse model-free and model-based feature extraction techniques.

Main Methods:

• Utilized a novel deep convolutional neural network (CNN) model.
• Employed concatenated fusion of CNN with a fully connected model.
• Extracted features from human silhouette images (model-free) and joint data (model-based).
• Tested the system on the CASIA gait dataset (casia-A and casia-B).

Main Results:

• Achieved high accuracy rates of 99.8% on casia (B) and 99.6% on casia (A).
• Demonstrated superior performance compared to state-of-the-art studies.
• Showcased robustness under various covariate conditions.
• Evaluated multiple performance metrics including accuracy, specificity, and sensitivity.

Conclusions:

• The proposed CNN model offers a highly accurate and efficient solution for non-contact human authentication.
• The fusion of model-free and model-based features enhances gait recognition capabilities.
• The system provides a robust real-time authentication method suitable for diverse conditions.