Facial recognition techniques appeared as early as the 1960s when Woodrow (Woody) Bledsoe, a mathematician and computer scientist, first applied computers to the challenge of recognizing human faces. Working with Helen Chan and Charles Bisson at Panoramic Research, Inc (PRI), Bledsoe helped develop a system to manually extract and record the coordinate locations of various facial features (eyes, nose, hairline, mouth, etc.) from a batch of photos (such as a book of mugshots). The metrics were then input into a computer database. Given a new photo, the database determined which original photo most closely resembled the new photo. This manual approach to capturing facial characteristics had its limitations, but Bledsoe continued his work in pattern matching and automated reasoning and is considered one of the founders of AI.
In 1991, Matthew Turk and Alex Petland, from MIT’s Media Laboratory Vision and Modeling Group, had a breakthrough when – applying algorithms based on linear algebra – they were the first to use computers to automatically extract facial feature from photos. By 2001, Paul Viola of Mitsubishi Electric Research Labs and Michael Jones of Compaq released a paper detailing their rapid object detection algorithm – dubbed Viola-Jones. Used as a real-time face detector, Viola-Jones required full view frontal upright faces but could detect faces in real time with high accuracy. It became the standard for facial detection for many years until it was overtaken by deep learning techniques.
The advent of deep learning techniques was a game changer for facial recognition. Trained neural networks (NNs) provided a significant jump in accuracy over programmed algorithms like Viola-Jones. In 2014, Facebook released Deepface, a nine-layer neural network with over 120 million connection weights (or coefficients) that have been trained by Facebook users using four million uploaded images (Figure 2). Deepface had 97.25% accuracy. In 2015, Google researchers developed FaceNet with an accuracy of 99.63%. FaceNet is really more than one neural network graph. It is based on at least two convolutional neural networks (CNNs).