Although most of the artificial intelligence has so far stuck in the longing for good, the arrival of artificial intelligence seems to be much faster than previously thought. The report on the government work of the two sessions clearly put forward that we will speed up fostering and expanding emerging industries, fully implement the strategic plan for the development of new industries, speed up R & D and transformation of new materials, artificial intelligence, integrated circuits, biopharmaceuticals, and fifth generation mobile communications, Strong industrial clusters. This is the first time AI has been officially listed in the "Report on the Work of the Government." From a policy perspective, this is also the government's recognition and support for this emerging industry after the Ministry of Science and Technology staunchly supported artificial intelligence last year The strongest tone. Qu Zongfeng, vice president of China Household Electrical Appliances Research Institute, also said: "The smart home industry into the next explosion, the symbol is the artificial intelligence in the IoT platform, a wide range of applications." In the case of hot artificial intelligence, biometrics As an important research area of ​​artificial intelligence, can it usher in the outbreak of artificial intelligence boom?

Under the artificial intelligence vents can usher in the outbreak of biometrics?

AI's past lives

Artificial intelligence is a new technological science that studies and develops theories, methods, techniques, and applications that extend and expand people. In the words of McCarthy, an associate professor at Dartmouth University who is the concept creator, it is "the science and engineering that makes intelligent machines, especially intelligent computer programs."

Artificial intelligence can be roughly divided into three stages, of which the most basic is computational intelligence, which means that machines can calculate and transmit information as human beings. The main value of artificial intelligence is to help human beings to store and process massive data rapidly. Cognitive and perception. Secondly, it is to perceive intelligence, that is to make the machine perceive the outside world, understand the image and understand the sound, and make some judgments to take actions such as face recognition, fingerprint recognition, speech recognition, image recognition, Target detection, sequence analysis and so on. The main value lies in that it can help human beings to accomplish the work of "seeing" and "listening" efficiently. The advanced stage of artificial intelligence is cognitive intelligence. Expecting that the machine has the ability to act independently and can think like a human And take the initiative to take initiatives including self-driving cars that are fully autonomous and autonomous robots with the goal of fully complementing or replacing human work.

Artificial intelligence is not a new technology. As early as the 1950s, the concept of artificial intelligence was proposed and identified as the field of research, which has been 60 years old. In recent years, the dramatic increase in processor performance, continuous optimization of machine learning algorithms, and the rapid development of big data and cloud technologies have all injected a great impetus into the outbreak of artificial intelligence. Whether it is Google, Facebook, Amazon or domestic Baidu, Alibaba and other influential technology companies, have shown their enthusiasm for artificial intelligence. In the past year, AlphaGo defeated go-world champion Li Shishi, the concept of artificial intelligence is even becoming a household name.

An Important Area of ​​Artificial Intelligence - Biometrics

Research in the field of artificial intelligence includes robotics, speech recognition, image recognition, natural language processing and expert systems.

Biometrics is the simulation of human intelligence through the machine to compare the biological characteristics and behavioral characteristics of the collected human body with the registration information so as to realize the personnel identification and identification. Biometric authentication is more secure, confidential and convenient than traditional authentication methods. Biometrics technology has the advantages of not easily forgotten, good anti-counterfeiting performance, not easy to forgery or theft, portable "carry" and available anywhere. In recent years, the accuracy of biometrics such as face recognition has been significantly improved through in-depth study, in-vivo testing and multidimensional verification.

Through the face recognition to see the depth of learning

Face recognition is different from the traditional traditional pattern recognition, face images are more complex, contains a wealth of level information, the data space dimension is high. The difference between the images of the same person under different poses and different lighting conditions is often greater than the difference between images under the same imaging conditions of different people. Therefore, it becomes extremely difficult to accurately distinguish different people.

In the face recognition algorithm based on traditional pattern recognition and machine learning methods, the performance of good image recognition is good, but the single algorithm is easily affected by many factors, such as light, attitude, age, etc. Generally, face recognition Shape features, face gray features and facial skin texture features and other features of fusion methods to effectively solve this problem.

The 1981 Nobel laureates David Hubel and Torsten Wiesel found that the information processing of the human visual system is hierarchical. From the Retina, the edge features are extracted through the low-level V1 area to the basic shape of the V2 area or a part of the target, to the top of the whole target (such as a face), and to the higher level PFC ( Prefrontal cortex) to determine the classification and so on. In other words, the features of the upper-level are the combination of the lower-level features. The expression of the features from the lower level to the upper level is more and more abstract and conceptualized, that is, the semantic or the intention is more and more expressed.

This discovery inspired people to think about the nervous system further. The working process of the brain may be a process of continuous iteration and constant abstract conceptualization. For example, starting with raw signal intake (pupil ingestion) followed by initial processing (some cells in the cerebral cortex detect the edges and the direction) and then abstraction (the brain determines the shape of the object in front of you, such as an oval) and then further Abstraction (the brain further determines that the object is a human face), and finally identifies the person in front of him.

Deep Learning attempts to mimic the workings of the human nervous system by combining many nonlinear functions in layers. For example, in the field of computer vision, the deep learning algorithm is a shallow learning from the original image to get a low-level expression, such as edge detectors, wavelet filters, etc., and then based on these low-level expression, through linear or non-linear combination , To obtain a high-level expression, the formation of more abstract high-level features (or attribute categories). If the corresponding category is human face, deep learning to achieve the task of face recognition.

The data show that the face recognition technology based on depth learning can further reduce the original rejection rate by 40% to 60% under the conventional false recognition rate, greatly improving the accuracy of face recognition, and at the same time reducing R & D staff work intensity. These laid the technical foundation for the large-scale application of face recognition technology.

Biometrics to help the development of artificial intelligence

With the vigorous national policy guidance, the continuous deepening of artificial intelligence theory and continuous breakthroughs in industrial applications, the artificial intelligence revolution will completely subvert the traditional industry business model, industrial chain and value chain and gradually affect all aspects of society, life and culture.

Data collection through biometrics is an important part of artificial intelligence applications. The use of biometrics to identify personnel is also the foremost link for human-computer interaction in the future. Biometrics will surely become an outbreak of artificial intelligence industry

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