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Updated: Nov 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

3D texture-based face recognition system using fine-tuned deep residual networks.

Siming Zheng¹, Rahmita Wirza Ok Rahmat², Fatimah Khalid³

¹CASD, Department of Multimedia, Putra Malaysia University, Sedang, Malaysia.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

Deep learning significantly improves 3D face recognition accuracy using 3D face texture, geometric invariants, and fine-tuned ResNet models. This advanced system achieves high accuracy on large datasets, outperforming traditional methods for efficient 3D face recognition.

Keywords:

3D textures Deep learning Face recognition system Fine-tuning Histogram of oriented gradients features Residual neural networks Tensorboard

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Area of Science:

Computer Vision
Artificial Intelligence
Biometrics

Background:

Rapid advancements in 3D photography have generated vast amounts of 3D image data.
3D face recognition is a key research area, but traditional methods struggle with large datasets, leading to decreased accuracy and inefficiency.
Deep learning offers a promising approach to overcome these limitations in 3D face recognition.

Purpose of the Study:

To propose an end-to-end face recognition system utilizing 3D face texture for enhanced accuracy.
To investigate the effectiveness of combining geometric invariants, histogram of oriented gradients, and fine-tuned residual neural networks (ResNet) for 3D face recognition.
To demonstrate the efficiency and improved performance of the proposed deep learning framework compared to traditional methods.

Main Methods:

Developed an end-to-end 3D face recognition system integrating 3D face texture, geometric invariants, and histogram of oriented gradients.
Employed fine-tuned residual neural networks (ResNet) for deep learning-based classification of 3D face data.
Evaluated the system's performance using the FRGC-v2 dataset.

Main Results:

The proposed system achieved a Top-1 accuracy of 98.26% and a Top-2 accuracy of 99.40% on the FRGC-v2 dataset.
Increased layers in the fine-tuned ResNet deep neural network led to improved recognition accuracy.
The deep learning framework required fewer iterations compared to traditional 3D face recognition methods.

Conclusions:

The proposed deep learning framework significantly enhances 3D face recognition accuracy, especially with large datasets.
The integration of 3D face texture, geometric invariants, and fine-tuned ResNet offers a robust solution for realistic 3D face recognition scenarios.
This approach provides a more efficient and accurate method for classifying large volumes of 3D face data.