The most recent method of face recognition by Google was done by the use of 200 million images and 8 million unique identities. This size of the dataset is considered to be very large in comparison to any other dataset that is publically available (Parkhi, Andrea and Andrew, 2015). Google has published the paper on the artificial intelligence system that claimed that FaceNet by Google is the most appropriate approach for recognizing the human faces. Evidences have shown that “FaceNet achieved nearly 100-percent accuracy on a popular facial-recognition dataset called Labeled Faces in the Wild, which includes more than 13,000 pictures of faces from across the web” (Harris, 2015, p. 1).
One of the significant approaches done by Google in face recognition was the Google Glass. The face recognition system of Google Glass meant to help people in social interaction. Face recognition is considered as the very first step of face to face interaction. Google had proposed the system of the wearable Google Glass that also worked as the social assistant and included those applications such as face detection, eye localization, face recognition and a user interface for personal information display. Google incorporated the artificial intelligence technique of deep learning that has been very effective in recognizing various objects. This is the reason that Google has acquired the deep learning start-ups in the recent years (Mandal et al., 2014).
Face recognition is the revolutionary technique that has made significant changes in the world of Internet. Face recognition can extract the useful recognition information from the digital images with the use of deep learning and neural networks. Deep learning and neural networks are very helpful in solving the problem of face recognition. The biometrics and nodes help in accurate face recognition. Facebook is the most popular social networking site that uses face recognition technology effectively. Facebook has been able to detect the faces that are tagged by the users. This paper discussed the technology of face recognition and reasons of its popularity. It concludes that face recognition technology has undergone significant changes in the analysis and algorithms. It can be concluded that face recognition has the ability of recognizing thousands of faces, as Google has also used this technique for its projects like Google Glass and FaceNet. Therefore, face recognition technology is highly dependent on deep learning and neural networks for accuracy and efficiency.