Project Background:
In biometric recognition, deep learning techniques are applied to identify and verify individuals based on their unique physiological or behavioral characteristics. Traditional biometric recognition systems rely on fingerprints, facial features, iris patterns, or voiceprints to authenticate individuals. However, these systems often face challenges such as variability in pose, illumination, and occlusion, which can degrade performance. Deep learning offers a powerful solution by leveraging neural networks to automatically learn discriminative features directly from raw biometric data. By training deep learning models on large datasets of biometric samples, such as facial images or voice recordings, these models can effectively capture complex patterns and variations, enabling more robust and accurate biometric recognition. This fusion of biometrics and deep learning has led to advancements in various applications, including access control, identity verification, forensic analysis, and surveillance. Moreover, the development of deep learning-based biometric recognition systems holds promise for enhancing security measures, improving user convenience, and advancing the capabilities of various industries and sectors.
Problem Statement
- Biometric traits can exhibit variability due to pose, illumination, and aging, challenging traditional recognition methods.
- Traditional biometric recognition systems may struggle to accurately identify individuals under adverse conditions such as low-light environments or partial occlusion.
- Biometric data collection raises privacy concerns, as it involves sensitive personal information that must be securely stored and processed to prevent unauthorized access or misuse.
- Deep learning-based biometric recognition systems are vulnerable to adversarial attacks, where subtle perturbations to input data can lead to misclassification or unauthorized access.
- Developing deep learning models for scalable across large populations while maintaining high accuracy poses a challenge due to the need for extensive training data and computational resources.
Aim and Objectives
- To enhance the accuracy, robustness, and security of biometric recognition systems by applying deep learning techniques.
- Develop deep learning models capable of accurately extracting discriminative features from biometric data such as facial images, fingerprints, or voice recordings.
- Improve the robustness of biometric recognition systems to variations in environmental conditions, pose, illumination, and occlusion.
- Address privacy concerns by developing privacy-preserving deep learning techniques for biometric data processing and storage.
- Mitigate the susceptibility of deep learning-based biometric recognition systems to adversarial attacks through robust training strategies and model defenses.
Contributions to Biometric Recognition using Deep Learning
- Deep learning models achieve higher accuracy in biometric recognition tasks than traditional methods.
- Exhibit greater robustness to variations in pose, illumination, and occlusion.
- Developing more secure biometric recognition systems, resistant to spoofing attacks.
- Enables the development of privacy-preserving biometric recognition solutions, safeguarding sensitive biometric data.
- Deployment in various practical applications, including access control and surveillance, enhances security and convenience.
Deep Learning Algorithms for Biometric Recognition
- Convolutional Neural Networks (CNNs)
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Siamese Networks
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Triplet Networks
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Deep Belief Networks (DBNs)
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Long Short-Term Memory Networks (LSTMs)
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Autoencoders
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Generative Adversarial Networks (GANs)
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Capsule Networks
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Recurrent Neural Networks (RNNs)
Datasets for Biometric Recognition
- LFW (Labeled Faces in the Wild)
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CASIA WebFace
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VGGFace2
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MegaFace
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CelebA
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MS-Celeb-1M
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FERET (Facial Recognition Technology)
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Cohn-Kanade (CK+)
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BioID
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VoxCeleb
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
