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Image Processing with Computer Vision Using Python

This case study explores a multi-functional image processing platform designed for diverse applications, including face cropping, theater pricing identification, proctoring, and audio-to-text conversion. The system leverages Python-based computer vision frameworks, advanced machine learning techniques, and a robust backend infrastructure for efficient and scalable performance. It demonstrates the potential of computer vision and audio analysis in addressing real-world challenges across various domains.

Challenges

  • Face Cropping for Images:

    Solution: Utilized the PyTorch MTCNN model to detect faces with precision, ensuring uniform cropping for consistent user profiles.

  • Theater Ticket Price Identification:

    Solution: Applied Keras image segmentation to classify seat pricing schemes based on distinct regions in the layout.

  • Auto Proctoring Module:

    Solution: Integrated face recognition for secure sign-ins and identity validation. Implemented gaze tracing, person counting, gadget detection, and audio activity analysis for robust monitoring. Built an API using Django Framework and deployed it with Docker for scalability.

  • Audio-to-Text Conversion:

    Solution: Developed a pipeline to download videos, extract audio, convert it to text, and store the data in a database. Built a Flask-based API for seamless interaction and containerized the pipeline using Docker.

Our Solutions

  • Facial Recognition and Liveness Detection:

    Built using Python DLIB DNN model, leveraging deep learning for high-accuracy face detection and recognition.
    Liveness detection ensures that login attempts are made by real users and not spoofed through static images or videos.

  • Time Optimization:

    Optimized the face recognition and liveness detection process to complete within 2.5 seconds, a significant improvement from the initial 16 seconds.
    Implemented model pruning and efficient inference pipelines to reduce latency.

  • Seamless ERP Integration:

    Integrated the solution into Next ERP to enable user enrollment, authentication, and access control directly from the platform.
    Ensured the facial recognition module aligns with existing user data workflows.

  • Scalable Cloud-Based Deployment:

    Deployed on AWS Server to handle high concurrency and ensure scalability for large enterprises.

Technology Slack

Flask

Django

Dlib

Keras

Pytorch

OpenCV

FFmpeg

Docker

Impacts

Location: Urban Mall Complex

 

  • Face Cropping for Web Applications:

    Uploaded individual and group images were processed using PyTorch MTCNN. Cropped images were resized and formatted for consistency across the web application.

    Outcome:

    Reduced manual effort by 90%, ensuring a uniform user experience.

  • Theater Price Identification:

    Images of theater layouts were segmented using Keras models to identify seating zones. Each zone was tagged with a pricing category based on its position and size.

    Outcome:

    Automated the process of mapping price categories, reducing errors and manual mapping time by 80%.

  • Proctoring Module:

    Proctoring APIs monitored student webcams and audio feeds during exams. Suspicious activities like gaze diversion, multiple faces, or unauthorized gadgets triggered alerts.

    Outcome:

    Enhanced credibility index accuracy to 95%, improving the reliability of online exams.

  • Audio-to-Text Conversion:

    Videos were downloaded from URLs and processed to extract audio using FFmpeg. Audio was transcribed into text using SpeechRecognition and stored in a database.

    Outcome:

    Enabled content creators to analyze video content effectively, reducing manual transcription time by 70%.

Benefits

  • Efficiency:

    Automated tasks like face cropping, ticket mapping, and transcription, reducing human intervention.

  • Accuracy:

    Leveraged state-of-the-art models for precise results in face detection and audio transcription.

  • Scalability:

    Dockerized APIs ensured seamless deployments across various environments.

  • Multi-Domain Application:

    Adapted to various fields, including education, entertainment, and data analytics.

Future Scope

  • Integration with AI Models:

    Use advanced models like Transformers (e.g., BERT) for context-aware text analysis.

  • Real-Time Processing:

    Implement real-time proctoring for live assessments.

  • Cloud Integration:

    Enable large-scale data processing by integrating with cloud platforms like AWS Lambda or GCP.

  • Advanced Gaze Analysis:

    Include metrics for detecting microexpressions and emotional cues during exams.

Conclusion

This image processing platform showcases how computer vision and audio processing can streamline workflows across diverse domains. By integrating tools like OpenCV, PyTorch MTCNN, Keras, and Docker, the solution achieves high accuracy, scalability, and adaptability, paving the way for future innovations.