Reconocimiento facial Jobs

I want to build a simple yet reliable mobile application that lets my field-workers mark their attendance the moment they snap a photo. The app must run smoothly on both Android and iOS and use face-recognition to validate each selfie automatically. As soon as the match is confirmed, the system should time-stamp the record and tag the exact GPS coordinates so I can verify where the employee was standing when the photo was taken. Core flow 1. Worker opens the app, the camera launches instantly. 2. They take a photo; the face-recognition engine checks it against a stored profile. 3. If the face matches, attendance is logged right away together with latitude, longitude and time. 4. All data syncs to a secure back-end; if the phone is offline it queues the record and uploads once a ...

I'm seeking an experienced hardware designer to create and manufacture a face recognition-based attendance terminal. The system should be built on the 8M Plus from NXP and run on a Linux platform. Key Requirements: - 5-inch color LCD touchscreen - Dual-camera setup (RGB + IR) for live face detection - LAN and Wi-Fi connectivity - Advanced encryption for high security - 4G and 5G module integration from qua tel - Input and outputs for Access control - Card Reader - Built-in battery back-up Ideal Skills: - Experience with NXP processors - Proficiency in embedded Linux-based systems - Knowledge in camera integration and security protocols - Hardware design and manufacturing experience - Designing the drivers and firmware with UI design experience Please provide a portfolio showcas...

The goal is to deliver a stand-alone Python desktop application that records classroom or workplace attendance through real-time face recognition. The stack is fixed: Python 3, OpenCV, and the standard Tkinter GUI toolkit. Core flow 1. A compact Tkinter window opens with clearly labelled buttons. The interface must, at a minimum, let a supervisor register new users. 2. During registration the system captures multiple webcam frames per person, automatically saving at least 20 crisp face crops for stronger training data. 3. An LBPH model is trained on those images and stored locally; subsequent launches should reload the latest model without retraining. 4. When recognition mode is active the app checks each detected face against the model and writes a time-stamped entry to a CSV fil...

I need Immich running on my QNAP TS-216G so that every original image and video, the PostgreSQL database, and all generated thumbnails live permanently on the NAS itself. Heavy tasks—face recognition, video transcoding, metadata extraction—should be pushed to my Windows-based desktop with an NVIDIA GPU and handled in real time over the network. Once processing is done, the NAS must still serve and display content without relying on the PC being online. Here’s what I’m after: • Install and configure Immich (Docker or native, your choice) on the QNAP with all persistent volumes mapped to NAS storage. • Set up a clean mechanism for off-loading compute jobs to the Windows PC: this could be a companion Immich micro-service stack, Docker over WSL2, or any ...

The goal is to deliver a stand-alone Python desktop application that records classroom or workplace attendance through real-time face recognition. The stack is fixed: Python 3, OpenCV, and the standard Tkinter GUI toolkit. Core flow 1. A compact Tkinter window opens with clearly labelled buttons. The interface must, at a minimum, let a supervisor register new users. 2. During registration the system captures multiple webcam frames per person, automatically saving at least 20 crisp face crops for stronger training data. 3. An LBPH model is trained on those images and stored locally; subsequent launches should reload the latest model without retraining. 4. When recognition mode is active the app checks each detected face against the model and writes a time-stamped entry to a CSV fil...