Video Annotator
In a facial recognition project for a leading AI data company, I performed high-precision video annotation tasks using tools such as Mindrift. The scope of the project involved identifying, labeling, and tracking human facial features across thousands of video frames. I applied bounding boxes and polygons to annotate faces in various angles, lighting conditions, and crowd scenarios, helping train AI models to detect and verify identities with high accuracy. The project demanded strict adherence to privacy standards, annotation consistency, and a QA accuracy rate above 95%. I collaborated remotely with an international team and actively participated in continuous quality reviews, ensuring that all annotations met the client's detailed guidelines for training facial recognition systems in real-world environments.