Ionic Selfie Pose Verification Task
Budget: $750 – $1,500 USD
https://docs.google.com/document/d/1ifTH6dI3ms1beuOA4lPQMFdfxkqKUCFdZGwRohcZ6k0/edit?usp=sharing
Make fully custom UI screens using our component library for standard elements (e.g., buttons, modals) that guide users through a linear flow:
(1) display an input example image with clear gesture instructions;
(2) prompt camera/video activation with permission handling;
(3) provide live preview with real-time overlays for pose matching and auto-capture 3-5 frames upon detection;
(4) process via configurable backends and show success/failure feedback with retries. The flow starts video recording on activation and outputs the verified user image (JPEG/PNG) plus the full capture video (MP4, <10MB) with metadata (e.g., timestamp, confidence). Develop this as a shareable Angular module with Ionic integration for Android/iOS/web compatibility via Capacitor, with a runtime config service (e.g., enableBackends(['amazon', 'azure', 'facetec'])) to toggle Amazon Rekognition Face Liveness, Azure Face Liveness, or FaceTec 3D Liveness SDK; include error handling for connectivity, lighting, and accessibility (e.g., WCAG-compliant prompts), plus automatic data deletion after processing.
Make fully custom UI screens using our component library for standard elements (e.g., buttons, modals) that guide users through a linear flow:
(1) display an input example image with clear gesture instructions;
(2) prompt camera/video activation with permission handling;
(3) provide live preview with real-time overlays for pose matching and auto-capture 3-5 frames upon detection;
(4) process via configurable backends and show success/failure feedback with retries. The flow starts video recording on activation and outputs the verified user image (JPEG/PNG) plus the full capture video (MP4, <10MB) with metadata (e.g., timestamp, confidence). Develop this as a shareable Angular module with Ionic integration for Android/iOS/web compatibility via Capacitor, with a runtime config service (e.g., enableBackends(['amazon', 'azure', 'facetec'])) to toggle Amazon Rekognition Face Liveness, Azure Face Liveness, or FaceTec 3D Liveness SDK; include error handling for connectivity, lighting, and accessibility (e.g., WCAG-compliant prompts), plus automatic data deletion after processing.