This is the part of Gen AI workshop recently we conducted in association with PSNA college and collaborators.

1. Face Recognition
Generative AI Using Azure

2. Introduction
Harnessing the power of AI using Microsoft Azure
• Definition of Face Recognition
• Importance in Various Fields (Security, Marketing, Healthcare, etc
• Potential Applications
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3. Definition of Generative AI
• Gen AI possess advanced capabilities in learning, reasoning, and
adaptation, often approaching or surpassing human-level intelligence
across a broad range of tasks and domains
• Power of autonomous decision-making compared to earlier generations
of AI.
• Gen AI represents a significant advancement in the field of artificial
intelligence and holds the potential to revolutionize various industries
and aspects of society.
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4. How does Gen AI work?
• Learn Pattern in a dataset
• Training
• Deep learning, Adversarial learning, Reinforcement learning
• Deep learning is a subfield of machine learning that involves the
training and usage of artificial neural networks to perform various tasks
• Adversarial learning involves iterative optimization o
• Reinforcement learning where an agent learns to make decisions by
interacting with an environment with feedback and optimization
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5. Models used in Gen AI
Generative Model
• try to understand the structure of the data and uses it to generate new
data similar to the original data
Discriminative models
• focus on the differences between the data
• boundary that separates the different categories of data
• eg: image generation, text generation, and even generating
realistic-sounding speech
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6. Discriminative Vs Generative
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7. GAN
• A generative adversarial network (GAN) is a deep learning architecture.
It trains two neural networks to compete against each other to generate
more authentic new data
Style GAN
DALL-E 7

8. Transformer Model
(processes i/p sq) (gen o/p seq))
Bidirectional Encoder Representations from Transformers
Generative Pretrained Transformers 8

9. Uses of Generative models
• Text Generation
• Sentiment Analysis
• Image Generation and Enhancement
• Video creation
• Code Generation
• Speech to Speech conversion (STS)
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10. • Text-to-Speech generation (TTS)
• Audio generation
• Synthetic data generation and augmentation
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11. Use cases of generative
AI models across
domains
Use cases of Generative AI models across domains
• Healthcare
• Art and Animation
• Marketing and Sales
• Software Programming
• Finance
• Manufacturing
• Entertainment
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12. Prominent examples of Generative AI tools
• Chat GPT
• GitHub Copilot
• Pictory.AI
• Midjourney
• Wordtune
• Gretel
• Genie AI
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13. Azure AI Services
Introduction to Azure AI Services
Overview of Azure Cognitive Services
Importance of Azure for AI Development
Azure Face API Azure Custom Vision
Azure Machine
Learning
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14. Azure Face API
• Used in security systems, access control, user authentication,
personalized experiences, and content moderation.
• Face Detection
• Face Verification
• Face Identification
• Face Recognition
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15. Azure Custom Vision
• Pre-built AI models that provide capabilities for vision, speech,
language
• Computer Vision: extract information from images and videos, including
image analysis, object detection, image classification, and optical
character recognition(OCR).
• Language Services: Offers natural language processing (NLP)
capabilities, including text analysis, sentiment analysis, language
translation
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16. Azure Machine Learning
• cloud-based platform that enables data scientists and machine learning
practitioners to build, train, deploy, and manage machine learning
models at scale
• offers a wide range of tools and capabilities to streamline the
end-to-end machine learning workflow, from data preparation to model
deployment.
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17. Face Recognition Generative AI
• The integration of generative models with face recognition systems can
lead to more robust, accurate, and versatile systems capable of
performing well in a wide range of scenarios and conditions
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18. Steps to create a face recognition system using Generative AI
• Data Collection and Preparation
• Train a Generative Model(GAN)
• Train a Face Recognition Model
• Choose a suitable face recognition algorithm or model architecture,
such as a convolutional neural network (CNN) trained on embeddings
of facial features.
• Train the face recognition model to learn discriminative features that
distinguish between different individuals in the dataset.
• Integration of Generative Model with Face Recognition
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19. Facial Recognition System
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• Register User, Train Image, Trigger are simple Azure functions
∙ Training Faces of Individuals
∙ Identifying faces of Individuals

20. Case Studies
• Real-world Examples of Face Recognition Generative AI Projects
• Virtual Try-On in Fashion, Facial Expression Generation, Personalized
Avatar Creation, Facial Aging Simulation
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21. Ethical Considerations
• Ethical Implications of Face Recognition Technology
• Privacy Concerns and Data Security
• Accuracy and Misidentification
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22. Future Directions
• Biometric Face Recognition for fraud prevention
• Character Analysis
• Opportunities for Further Research and Innovation
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23. Summary of Key Points
• Importance of Azure in Advancing Face Recognition Generative AI
• Questions and Discussion
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24. Thank you
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