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Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image

Abeer Mohsin Saleh^1,2, Talal Hamoud¹

¹Damascus University, Damascus, Syria.

Journal of Big Data

|January 11, 2021

Summary

This summary is machine-generated.

This study adapted deep convolution neural networks (CNNs) with image augmentation (IA) for person recognition using gait features. The adapted model significantly improved accuracy, achieving 96.23% for 200 persons, demonstrating robustness to variations.

Keywords:

Convolution neural network Deep learning Gait model Image augmentation Person recognition

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Person recognition using gait is challenging due to pose variation, occlusion, and camera angle differences.
Existing methods often struggle with real-world complexities.
Gait features offer a unique biometric modality for identification.

Purpose of the Study:

To enhance person recognition accuracy using gait features by adapting deep convolution neural networks (CNNs).
To investigate the effectiveness of image augmentation (IA) in improving model robustness and performance.
To optimize CNN model design and parameters for gait-based person identification.

Main Methods:

Modified and adapted a deep convolution neural network (CNN) for gait-based person recognition.
Employed image augmentation (IA) techniques to expand the training dataset size and diversity.
Adapted CNN architecture, including layer types, number of layers, and normalization strategies.
Tested the adapted model on the Market dataset, which includes sequential images of individuals with varying gaits.

Main Results:

The adapted CNN model with IA significantly improved person recognition accuracy compared to models without adaptation.
For 200 persons, validation accuracy increased from 82% without IA to 96.23% with IA.
The IA technique enhanced model robustness against variations like image quality, resolution, rotations, and carried objects.
For 800 persons, validation accuracy reached 93.62% without IA, indicating strong performance with larger datasets.

Conclusions:

Adaptation of CNNs and the use of image augmentation are effective strategies for improving gait-based person recognition.
The developed approach demonstrates robustness to common real-world variations, making it suitable for practical applications.
Further research can explore more complex gait variations and larger-scale datasets for even greater accuracy.