Contact person: Business Client

Phone:Show

Email:Show

Location: Delhi, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I have a web application that needs a real-time face-camera module. The flow is straightforward: once the user activates the webcam, the system must detect the face instantly, crop it cleanly, perform a quick enhancement (brightness, contrast, denoise) and hand the processed frame over to my prediction pipeline. Because speed is critical, I want this working fully in the browser.

Detection should rely on a pre-trained model—nothing custom-trained from scratch—so feel free to tap into solutions such as MediaPipe, [login to view URL] Face Mesh, or another lightweight alternative that can run client-side without heavy downloads. The cropped and enhanced image then heads to an existing model endpoint; you only need to format the call and feed it the image tensor/base64 that the endpoint expects.

Key deliverables
• A single-page web demo (Vanilla JS or a framework like React is fine) that opens the webcam, shows the live video with bounding box feedback, and streams the cropped, enhanced face to the model.
• Clean, well-commented source code plus a brief README explaining how to swap in a different pre-trained detector if needed and how to point the fetch/axios call to another endpoint.
• Performance benchmark indicating average FPS on a standard laptop browser and an example prediction JSON coming back from the model to prove end-to-end flow.

Acceptance criteria
1. Detects and crops the primary face in under 120 ms per frame on Chrome and Firefox.
2. Enhancement visibly improves low-light frames without introducing color cast.
3. No external server is required for detection or enhancement; everything runs client-side except the final prediction call.
4. Code builds and runs with one command (`npm install && npm start`) and passes lint/tests if included.

If you have prior work with WebRTC, [login to view URL], or [login to view URL], that will help us move faster. Let me know what pre-trained model you plan to use and how you will keep the bundle size lean." (client-provided description)