At this stage, the popular image recognition algorithm is the deep learning method, and the deep learning model belongs to the neural network. The history of the neural network can be traced back to the 1940s and was once popular in the 1980s and 1990s. The neural network attempts to solve various machine learning problems by simulating the brain's cognitive motivation. In 1986, Rumelhart, Hinton, and Williams published the famous back-propagation algorithm in Nature to train neural networks, which is still widely used today.
However, due to various reasons, most scholars gave up the neural network for quite a long period of time and switched to such classifiers as support vector machines, Boosting, and nearest neighbors. These classifiers can be modeled using a neural network with one or two hidden layers and are therefore referred to as shallow machine learning models. They no longer simulate the cognitive mechanism of the brain; instead, different systems are designed for different tasks, and different hand-designed features are used, such as Gaussian mixture model and hidden Markov model for speech recognition, and SIFT features for object recognition. Face recognition uses LBP features and pedestrian detection uses HOG features.
The most influential breakthrough in deep learning in the field of computer vision occurred in 2012. Hinton’s research team used deep learning to win the ImageNet image classification competition. ImageNet is one of the most influential games in the field of computer vision today. Its training and test samples come from internet pictures. The training sample is over one million. The task is to divide the test sample into 1000 categories. Since 2009, many computer vision groups including industry have participated in the annual competition. The methods of each group have gradually converged; in 2012, the traditional analog identification methods adopted by the 2nd to 4th teams all adopted the accuracy rate. The difference does not exceed 1%, and the Hinton research team that entered the competition for the first time uses a deep learning approach with more than 10% accuracy in the second place. This result produced a great shock in the field of computer vision, setting off a boom in deep learning.
The biggest difference in deep learning compared to traditional pattern recognition is that it automatically learns features from big data, rather than using hand-designed feature models. In the various applications of pattern recognition in the past decades, the characteristics of manual design are in a dominant position. It mainly relies on the designer's experience and knowledge, and it is difficult to make use of the advantages of big data; due to the manual adjustment of parameters, the design of features is only allowed. A few parameters appear. The advantage of deep learning is obvious. Big data can contain thousands of parameters. The more data used to train deep learning, the more robust and generalized the deep learning algorithm.
At present, the training data for deep learning algorithms are generally hundreds of thousands and millions. Like some IT giants in the Internet industry, their training data will be tens or even hundreds of millions of dollars. This is also foreign, such as Google, Facebook, Microsoft, etc., and domestic IT companies such as Baidu and Tencent have certain advantages in the application of deep learning algorithms. However, the training data used by IT companies and security companies is different. IT giants own the Internet, and security companies have security big data. The focus of the two image recognition technologies is also different. The IT giant's face recognition technology is to serve their business goals, such as image retrieval, identity authentication, driverlessness, etc., while security companies are mainly concerned with face recognition technology. Public safety applications.
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