AI Smart Mirror
Budget: ₹12,500 – ₹37,500 INR
I am building the first working prototype of ZENVIA’s AI-powered smart mirror and need a computer-vision specialist to own the core software stack for the next 3–4 weeks. The mirror will run on Windows and must:
• Log users in via face recognition with high accuracy—false accepts/rejects should be rare enough that manual override is almost never needed.
• Overlay chosen outfit images in real time so the user can “try on” clothes virtually (initially a simple 2-D overlay aligned to the torso; we will refine later).
Tech stack
Python 3.x with OpenCV is non-negotiable. If you can fold in MediaPipe for landmark detection that will be a welcome bonus and will likely boost performance.
Data
I do not yet have any datasets—neither for face recognition nor for outfit imagery—so part of the assignment is advising on, sourcing, or quickly curating open-source data that will let us reach production-quality results without violating licenses.
Scope of work
1. Design and train (or fine-tune) a high-accuracy face recognition model suited to a single-user device.
2. Build a lightweight Windows application that detects the user’s face, verifies identity, and immediately unlocks the interface.
3. Implement a modular overlay pipeline: load PNG outfit layers, align them to the live camera feed, and render at interactive frame rates.
4. Package everything so that it can run on commodity hardware with a standard webcam.
Deliverables
• Source code with clear setup instructions
• Pre-trained model files
• Minimal GUI demo (Python/Qt or similar)
• Short video demo showing login and try-on flow
• One-page hand-off document describing any further training steps
Timeline & milestones
Week 1: Data strategy + baseline model
Week 2: Face recognition integrated into Windows demo
Week 3: Outfit overlay module + performance polish
Final days: Testing, bug fixing, hand-off
Budget is INR 60k–80k; I’m happy to split it across the milestones above. Please share links or repos from past AI/computer-vision projects—particularly face recognition or AR/try-on work—so I can see your style and code quality.
Looking forward to collaborating on this rapid build.
• Log users in via face recognition with high accuracy—false accepts/rejects should be rare enough that manual override is almost never needed.
• Overlay chosen outfit images in real time so the user can “try on” clothes virtually (initially a simple 2-D overlay aligned to the torso; we will refine later).
Tech stack
Python 3.x with OpenCV is non-negotiable. If you can fold in MediaPipe for landmark detection that will be a welcome bonus and will likely boost performance.
Data
I do not yet have any datasets—neither for face recognition nor for outfit imagery—so part of the assignment is advising on, sourcing, or quickly curating open-source data that will let us reach production-quality results without violating licenses.
Scope of work
1. Design and train (or fine-tune) a high-accuracy face recognition model suited to a single-user device.
2. Build a lightweight Windows application that detects the user’s face, verifies identity, and immediately unlocks the interface.
3. Implement a modular overlay pipeline: load PNG outfit layers, align them to the live camera feed, and render at interactive frame rates.
4. Package everything so that it can run on commodity hardware with a standard webcam.
Deliverables
• Source code with clear setup instructions
• Pre-trained model files
• Minimal GUI demo (Python/Qt or similar)
• Short video demo showing login and try-on flow
• One-page hand-off document describing any further training steps
Timeline & milestones
Week 1: Data strategy + baseline model
Week 2: Face recognition integrated into Windows demo
Week 3: Outfit overlay module + performance polish
Final days: Testing, bug fixing, hand-off
Budget is INR 60k–80k; I’m happy to split it across the milestones above. Please share links or repos from past AI/computer-vision projects—particularly face recognition or AR/try-on work—so I can see your style and code quality.
Looking forward to collaborating on this rapid build.