Semantic Segmentation in Facial Recognition

Facial recognition technology is becoming a feature in our everyday lives. More and more companies are using facial recognition technology to detect and identify faces for various use cases. These include monitoring a driver’s facial expression for safe driving and unlocking smartphones, just to name a few. 

Using specific image annotation techniques e.g. semantic segmentation and landmark annotation, logical computer vision models for facial recognition are probable. These unique data labels to aid in identifying the shape and variation of objects. 

Keypoint Annotation for Facial Features Detection

Also referred to as landmark annotation, keypoint annotation is suitable for building AI-based facial recognition applications. By making high-quality keypoint annotations across different classes for pinpoint detection of facial features/attributes. 

Landmark annotation involves labeling a facial image using key points placed at specific locations on the face. This aids the model to identify the facial expression or gesture to effectively train a logical AI bases facial recognition application. Landmarking aids in determining the authentic density of an object in specific areas. 

Semantic Segmentation for Facial Recognition

Semantic segmentation is employed to produce datasets crucial to building self-driving cars and ADAS semi-autonomous cars. Also known as image segmentation, its use cases are ever-increasing given the evolving AI technology. 

At Impact Outsourcing, we offer the best data annotation services at a fraction of the total cost. By trusting us, your datasets will be of the highest quality, perfect for training logical AI/ML models. Be it in healthcare, automotive, robotics, or agriculture, Impact Outsourcing has the solutions to build your world-class AI/ML application.