Are you thinking of developing artificial intelligence or machine learning-based models to solve business issues without human intervention?
However, computer vision-based models have been playing an essential role in creating a safer world. Before you consider using facial recognition technology, you need to understand data annotation, its types, and how it supports facial recognition projects?
What is Data Annotation?
Data annotation is a process that aims to transform content in different file formats to be recognizable by machines such as images, video, and text. It is a simple process designed to label or annotate an object to detect it by using algorithms.
The importance of data annotation lies in its ability to eliminate the possibility of minor human errors, which could be disastrous. It also helps in ensuring the scalability of the AI or ML projects. The annotation of data is to use the right tools and techniques and train the computer-vision-based ML model.
Types of Data Annotation:
The annotation of data into image, video, and text is to ensure the accuracy of data and divides data annotation into three types:
- Image Annotation
Image annotation requires the creation of data sets to make the objects detectable to the computer vision for machine learning. Different image annotation methods mainly include 3D point annotation, polygon annotation, landmark annotation, 3D cuboid annotation, semantic segmentation, and bounding box.
- Video Annotation
Video annotation uses frame-by-frame objects to annotate the data to create training data for visualization of autonomous or self-driving cars. The purpose of video annotation is to estimate the movements of objects.
- Text Annotation
Text Annotation involves the use of metadata for generating keywords to be recognized by computers. Text annotation aims to create a communication mechanism. AI chatbots and virtual assistant devices are used in text annotation to respond to questions to provide a user-friendly experience.
Data Annotation in Facial Recognition Projects
Facial recognition technology is a method of recognizing human faces by comparing the information in the database. Facial recognition technology involves using an algorithm comprising a large amount of data in the form of photos with human faces. It uses deep neural networks to train the system.
The quality and accuracy of training datasets in image and video annotation are essential in ensuring face recognition models’ proper functioning. In annotation, absolute precision is critical to ensure that AI models do not reproduce errors.
Facial recognition data points required for annotation include video annotation, landmark point annotation, key point annotation, and TagX annotation services.
The function of facial recognition technology depends on identifying emerging threats to recognize emotions and conduct sentiment analysis.
Facial recognition software uses 2D and 3D masks for the unique identification of facial features. The algorithm uses codes to verify the face and compare it with other faces patterns in the database. The system uses sequences of images to improve face recognition accuracy.