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RETRACTED: A deep facial recognition system using computational intelligent algorithms.

Diaa Salama AbdELminaam^1,2, Abdulrhman M Almansori¹, Mohamed Taha³

¹Department of Information Systems, Faculty of Computers and Artificial Intelligence, Benha University, Benha City, Egypt.

|December 3, 2020

Summary

This study introduces an advanced facial recognition (FR) system leveraging deep convolutional neural networks (DCNN) within fog and cloud computing environments. The developed system demonstrates superior accuracy and efficiency for smart city applications.

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Area of Science:

Computer Science
Artificial Intelligence
Biometrics

Background:

Facial recognition (FR) is crucial for smart city applications.
Robust FR algorithms are needed to overcome challenges like occlusions and pose variations.
Existing FR systems require enhancement for real-time performance and adaptability.

Purpose of the Study:

To develop a comprehensive facial recognition system using transfer learning in fog and cloud computing.
To enhance FR performance by utilizing deep convolutional neural networks (DCNN) for feature extraction.
To create a system capable of online learning and improving recognition accuracy over time.

Main Methods:

Implemented a facial recognition system integrating deep convolutional neural networks (DCNN) for feature extraction.
Employed transfer learning within a fog and cloud computing architecture.
Evaluated the system against Decision Tree (DT), K Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms.
Tested the system on three standard face image datasets: SDUMLA-HMT, 113, and CASIA.

Main Results:

The proposed DCNN-based FR system achieved superior performance across all evaluated metrics.
Achieved high accuracy (99.06%), precision (99.12%), recall (99.07%), and specificity (99.10%).
Demonstrated significant improvements over traditional machine learning algorithms in facial recognition tasks.

Conclusions:

The developed facial recognition system offers a robust and accurate solution for smart city applications.
The integration of DCNN with transfer learning in fog and cloud computing enhances FR system capabilities.
The system's ability to learn online and adapt to new data signifies its potential for real-world deployment.