Project Background:
Face recognition using deep learning revolves around developing highly accurate and efficient systems for identifying and verifying individuals based on facial characteristics. With the advent of deep learning techniques, particularly convolutional neural networks (CNNs), strides have been made in computer vision in tasks related to facial analysis. Face recognition has emerged as a critical application with widespread practical implications ranging from security and surveillance to authentication and personalization in various industries.
Deep learning-based approaches have shown remarkable success in learning discriminative features from facial images, enabling systems to achieve unprecedented accuracy and robustness in identifying individuals across varying conditions such as pose, illumination, and occlusion. Key objectives in this domain typically include improving recognition accuracy, enhancing the efficiency of recognition algorithms to support real-time applications, and addressing challenges related to scalability and privacy.
Problem Statement
- Enhance the accuracy of face recognition algorithms to ensure reliable and precise identification of individuals across various conditions.
- Develop techniques to improve the robustness of face recognition models against environmental factors like varying illumination background clutter.
- Optimize for efficient real-time processing, enabling quick and accurate identification in applications such as surveillance systems, access control, and mobile devices.
- Investigate methods to preserve privacy in face recognition systems for data anonymization, secure storage, and minimizing the risk of unauthorized access or misuse of facial data.
- Explore techniques for improving the generalization capability of face recognition models across different demographic groups, ethnicities, ages, and cultural backgrounds.
- Enhance the robustness of systems against adversarial attacks to remain resilient to deliberate attempts to deceive or manipulate the system.
Aim and Objectives
- Develop accurate and efficient face recognition systems using deep learning techniques.
- Enhance recognition accuracy across varying conditions.
- Optimize algorithms for real-time performance.
- Improve robustness to environmental factors such as lighting and occlusions.
- Preserve privacy and uphold ethical considerations.
- Enhance generalization across diverse demographic groups.
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Increase adversarial robustness against attacks.
Contributions to Face Recognition using Deep Learning
- Enhanced accuracy and robustness across diverse conditions.
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Optimized algorithms for real-time performance.
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Privacy-preserving techniques and ethical considerations.
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Improved generalization across demographic groups.
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Advancements in adversarial robustness against attacks.
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Enhanced interpretability and explainability of identification results.
Deep Learning Algorithms for Face Recognition
- VGG-Face
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FaceNet
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DeepFace
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DeepID
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SphereFace
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ArcFace
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CosFace
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CenterLoss
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MobileFaceNet
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LightCNN
Datasets for Face Recognition using Deep Learning
- LFW (Labeled Faces in the Wild)
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CelebA
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CASIA WebFace
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VGGFace2
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MS-Celeb-1M
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MegaFace
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IJB-A
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FG-NET
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Adience
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CALFW
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
