FaceTrack: Advanced Employee Attendance System with Facial Recognition

This project presents an AI-driven automated employee attendance system designed to transform attendance management in modern workplaces. By leveraging advanced deep learning techniques for real-time facial detection and identification, the system automates employee check-ins and check-outs, eliminating the need for traditional manual or card-based methods. This ensures enhanced accuracy, streamlined operations, and a seamless integration into existing workflows. With features like automated time tracking, secure data management, and a user-friendly interface, it redefines workplace efficiency and accountability.

Key features:

• Real-Time Facial Recognition: Employs an efficient custom-trained architecture inspired by FaceNet to accurately and efficiently identify employees from live video feeds, ensuring seamless attendance logging without manual intervention.

• Automated Attendance Management: Tracks employee entry and exit times with precision, calculates the total time worked, and stores all data securely in database for easy access and record-keeping.

• Anti-Spoofing Mechanism: Employs anti-spoofing technique to differentiate between live faces and spoofing attempts, ensuring security and reliability.

• Date-Based Record Filtering: Enables administrators to quickly filter and review attendance records by selecting specific dates through an intuitive dashboard date picker, streamlining data retrieval.

• Comprehensive Administrative Dashboard: Features a user-friendly interface that empowers administrators to view, manage, and update attendance records with ease, improving operational efficiency.

• Flexible Record Deletion: Includes an option to delete incorrect or outdated entries, ensuring the database remains clean and accurate for reporting and analysis.

• Robust Security with Login System: Implements a secure authentication mechanism, restricting access to the dashboard and attendance records to authorized personnel only, safeguarding sensitive employee data.

Technological Stack:

• Deep Learning Framework: TensorFlow for overall architecture development.

• Data Processing: NumPy, OpenCV for processing live video feed and face detection.

• Frontend Development: HTML, CSS, and Bootstrap for dashboard and user interface.

• Backend Integration: Initially Flask, later FastAPI for model API and server-side logic.

• Database and Storage: MySQL for efficient storage and retrieval of attendance records.

• Vector Database: Qdrant for efficient image embeddings storage and retrieval of employee records.

Simple illustration of the project:

***The code regarding this project can't be made available due to proprietary reasons***

Trendify: Intelligent Visual Shopping Assistant

This project presents an AI-driven visual product recommender system designed to enhance the shopping experience by delivering personalized product recommendations leveraging advanced visual analysis and deep learning techniques. It harnesses image-based feature extraction and user behavior patterns to intelligently suggest trending and relevant products, enhancing the overall shopping experience. By analyzing product aesthetics and user preferences and by providing visually driven, context-aware recommendations, this system aims to boost customer engagement and increase conversion rates.

Key features:

• Visual Similarity Search: Employs an efficient custom-trained architecture inspired by Deep Neural Network (DNN) architectures to extract visual features from product images, enabling recommendations based on visual similarity rather than just textual attributes.

• Context-Aware Recommendation: Combines image embeddings with user behavior data such as interactions, search queries, and click-through rates to tailor personalized suggestions that align with individual styles, enhancing user engagement and satisfaction.

• Real-Time Recommendation: Provides dynamic responsiveness, the engine delivers instant product suggestions as users interact with the platform. Whether scrolling through items or viewing specific products, recommendations update seamlessly in real time, creating a smooth and intuitive user experience.

• Multi-Modal Data Fusion: Leverages multi-modal comprehensive data fusion approach, seamlessly integrating visual data with complementary inputs such as purchase history, product ratings, and browsing behavior to enable the generation of precise and contextually relevant recommendations.

Technological Stack:

• Deep Learning Framework: TensorFlow for overall architecture development.

• Data Processing: Pandas, NumPy, OpenCV and Pillow for multi-modal data and image processing.

• Frontend Development: HTML, CSS, JavaScript for web app UI/UX.

• Backend Integration: Initially Flask, later FastAPI for model API and server-side logic.

• Database and Storage: Initially MySQL for structured user behavior data management.

Simple illustration of the project:

***The code regarding this project can't be made available due to proprietary reasons***

Future Enhancement:

• Augmented Reality (AR) Support: Incorporating AR previews to complement visual recommendations.

ImageHance: AI-Driven Visual Quality Elevation

This project introduces an AI-driven system that leverages advanced deep learning model to enhance image quality, improve details, and optimize visual appeal. The system was deployed on web platform initially and later was planned to integrate into mobile platform, offering users an intuitive platform to refine their images effortlessly. It addresses traditional image enhancement challenges by employing state-of-the-art deep learning model that analyzes and enhances images in real time, delivering professional-grade results with minimal user input.

Key features:

• Dynamic Color and Contrast Adjustment: Employs an efficient custom-trained architecture inspired by Zero-Reference-DCE-Net with more advancement for automatically optimizing lighting, color balance, and contrast to enhance the overall visual appeal.

• Detail Refinement: Enhances image clarity by sharpening fine textures, bringing out intricate details, and improving overall crispness. This feature ensures that subtle elements, such as fabric patterns, hair strands, or architectural details, are well-defined, delivering a sharper and more polished look to the image.

• Multi-Platform Availability: Initially deployed as a responsive local web application with plans for seamless mobile integration to ensure accessibility across devices.

Technological Stack:

• Deep Learning Framework: TensorFlow for overall architecture development.

• Image Processing: OpenCV and Pillow for image processing.

• Frontend Development: HTML, CSS, JavaScript for web app UI/UX.

• Backend Integration: Initially Flask, later FastAPI for model API and server-side logic.

• Mobile Integration: Flutter based mobile app for easy and user friendly interaction.

Simple illustration of the project:

***The code regarding this project can't be made available due to proprietary reasons***

AI-Powered Background Manipulation and Generation

The primary objective of this project was to create an intuitive and user-friendly system capable of delivering accurate and contextually relevant output to both employees and root-level users. This AI-powered tool redefines image manipulation by offering seamless background removal and custom background generation. Designed for both web and mobile platforms, the solution integrates cutting-edge technologies to deliver professional quality results for diverse use cases.

Key features:

• Seamless Object Detection and Background Removal: Employs an efficient custom-trained ROI detection architecture inspired by U2-Net with more advancement as well as incorporating morphological operations for precise subject isolation that handles complex elements such as hair, fur, and transparent objects with remarkable accuracy.

• AI-Driven Background Generation: Utilizes generative AI model to create high-quality and contextually relevant backgrounds as well as offers text-based commands to customize styles, themes, and visual elements, empowering users to create unique imagery.

• Multi-Platform Availability: Delivers a responsive interface for professional workflows on desktops and incorporates advanced image manipulation capabilities into a mobile app.

Technological Stack:

• Deep Learning Frameworks: TensorFlow for ROI detection and background removing architecture development. PyTorch for GenAI model development.

• Image Processing: OpenCV and Pillow for image processing and morphological operations.

• Frontend Development: HTML, CSS, JavaScript for web app UI/UX.

• Backend Integration: Initially Flask, later FastAPI for model API and server-side logic.

• Mobile Integration: Flutter based mobile app for easy and user friendly interaction.

Simple illustration of the project:

***The code regarding this project can't be made available due to proprietary reasons***

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