Facial Image Processing for Optimized Recognition and age estimation in MATLAB

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B.Manoj Kumar Raju; M. Vamsi Krishna; G.Jagadeesh; V. Mohammed Ibrahim

Facial Image Processing for Optimized Recognition and age estimation in MATLAB

Authors: B.Manoj Kumar Raju, M. Vamsi Krishna, G.Jagadeesh, V. Mohammed Ibrahim

Unique Paper ID: 175462
Volume: 11
Issue: 11
PageNo: 4221-4224

Keywords: Face Dataset Image Processing Techniques Deep Learning Techniques Convolution Neural Network Classification Accuracy.

Abstract:
This paper proposes a hybrid Convolutional Neural Network (CNN) model to enhance facial recognition and age estimation accuracy. Input images are preprocessed by resizing and converting to grayscale, followed by data augmentation to improve generalization. The dataset is divided into training and validation sets. The CNN architecture integrates multiple convolutional layers with batch normalization and ReLU activation, followed by pooling layers for feature extraction. Trained using Stochastic Gradient Descent with Momentum (SGDM) optimizer (learning rate: 0.001, batch size: 25, 100 epochs), the model classifies facial identities (e.g., 'Person 1,' 'Person 2') and age ranges (e.g., 20-25, 30-35). Performance is evaluated via confusion matrices and accuracy metrics. The hybrid CNN leverages deep feature extraction, achieving robust classification for biometric security and age-based authentication. Results demonstrate improved recognition accuracy, validating its efficacy for real-world applications

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4221-4224

Facial Image Processing for Optimized Recognition and age estimation in MATLAB

Available:https://ijirt.org/article?manuscript=175462

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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