In 2017, Apple's new mobile phone iPhone X uses Face ID face recognition to unlock. Previously, Xiaomi Note3 and Vivo V7+ also launched a smart phone with face recognition function. This marks the acceleration of face recognition technology into the consumer class.
Significant changes are taking place in many areas other than mobile devices. Wuhan Railway Station announced that it will fully face the station; Baidu announced that it has signed a strategic cooperation agreement with Capital International Airport to create a smart airport with a face-to-face boarding; Alipay announced a commercial brush face payment; a large number of hotels in Hangzhou are free of ID cards and can be accommodated by brushing; Jingdong Suning opened the face payment; the Agricultural Bank of China piloted the “painting face withdrawal†at the ATM, and has issued a notice requesting the nationwide promotion of face-lifting, which will be installed for 24064 branches, 30089 ATMs and 100,000 ATMs nationwide. Face recognition system; China Merchants Bank has also launched the “swipe face withdrawal†option in the ATM cash machine system in key cities in the near future. Users can not use bank cards or ID cards, and do not need to enter bank accounts. Withdrawal. It can be seen that face recognition technology has accelerated penetration into security, banking, payment and many other fields, and has entered the era of consumer-level outbreaks from government-level applications and business-level applications, verifying the huge market demand for face recognition technology. Broad application prospects.
According to Yole data, the global face recognition market is expected to grow from $4.05 billion in 2017 to $7.76 billion in 2022, with a compound annual growth rate of 13.9%. The main factors driving market growth include: iPhone X drives more smartphones to integrate 3D face recognition, the growing demand for monitoring in public places, and the growing use of face recognition technology by various industries such as government agencies.
Advantages of face recognition technologyThe engineering application of face recognition began in the 1960s. After more than 50 years of development, face recognition technology has made major breakthroughs, and many classic algorithms and face libraries have appeared one after another. At present, the highest correct rate of face recognition system can reach 99.5%, and the correct rate of recognition of human eyes under the same conditions is only 97.52%, and the accuracy of face recognition has been more accurate than the naked eye.
Face recognition is roughly divided into two application mode four-step processes. The two modes include 1:1 alignment and 1:N recognition, 1:1 is to compare the similarity of two faces, only need to determine whether to authorize the person; 1:N is to identify whether the object is in the face database. The four processes are, in turn, face detection, in vivo detection, face feature extraction, and face matching recognition.
Compared with other biometrics, face recognition technology has unique technical advantages in practical aspects, mainly reflected in the following aspects:
1. Non-contact: The collection of face images is different from fingerprints and palm prints. It is necessary to contact the palm-grain collection equipment. The collection of palm prints is not hygienic except for certain wear and tear on the equipment. The device for collecting face images is a camera and does not need to be touched.
2, non-intrusion: the collection of face photos can be automatically taken using the camera, without the need for staff intervention, and does not need to be matched by the collector, just in front of the camera through the normal state.
3. Friendly: The human face is a biological feature that is exposed after a person is born, so its privacy is not as strong as palm prints and irises, so the collection of faces is not as unacceptable as the palm print collection. .
4, intuitive: We judge who is a person, by looking at this person's face is the most intuitive way, unlike the palm print, iris and other related fields experts can be judged.
5, fast: the face collection from the camera surveillance area is very fast, because its non-intervention and non-contact, the face acquisition time is greatly shortened.
6, simple: face acquisition front-end equipment - camera can be seen everywhere, it is not a dedicated device, so easy to operate.
7. Scalability is good: its collection end can completely adopt the camera equipment of the existing video surveillance system. The scalability of the back-end application determines that face recognition can be applied to access control, blacklist monitoring, face photo search, etc. field.
Deep learning and 3D vision boost face recognition development
For a long time, insufficient technological innovation, limited application promotion, and high price and cost are the three factors that restrict the development of face recognition. Research on face recognition algorithm optimization, lightweight team is less, algorithm optimization has not been completed, maintaining a certain speed and accuracy in the case of low power consumption is a difficult point in the development of face recognition applications. Therefore, in recent years, face recognition has been widely used in government departments such as immigration clearance and airport security inspection, but it has not really entered the wider commercial application field.
First, on the technical level, the accuracy of face recognition and the original innovation of the core algorithm are insufficient, and the technical standards are not perfect. To quickly and accurately complete face recognition, it is necessary to solve many technical difficulties:
Self-physiological changes. In the process of face matching, if there is a large physiological change in the face stored in the database itself, such as undergoing shaving, changing hairstyles, wearing glasses and other changes. Even if the appearance does not change too much, people who produce a lot of expressions through changes in the face may cause the comparison to fail.
External environmental impact. Faces are affected by many external factors: when shooting at different angles, the visual images of faces vary greatly; they are easily affected by lighting conditions, such as day and night, indoor and outdoor lighting.
In addition to these two situations, there are also extreme situations such as artificial cosmetic behavior and twins. How to avoid the influence of these external factors on face recognition speed and recognition effect has always been the focus of research.
Face recognition technology is difficult
Second, in terms of application, face recognition is basically limited to government departments such as the entry and exit management of the public security department, and has not penetrated into large-scale commercial-level applications and personal consumption levels. At the application level, although the accuracy of many face recognition technologies has reached 99% or even 99.5% in research conditions such as laboratories, these technologies and solutions are difficult to reach the practical application level. In the process of landing face recognition technology, it is necessary to consider different scenarios. In the actual landing process, other biometrics such as fingerprint recognition are not easily affected by external factors due to their mature technology. They have already been applied to attendance certification and smart phone account login. The mature development of alternatives also restricts the industrialization of face recognition. process.
Third, in terms of price, the lack of market competition and immature technology have led to high prices. Due to the immature technology, face recognition technology has not been applied to business scenarios and consumer-level fields. Most of them are applied to government and public security departments, and are delivered in a system-integrated manner. The cost and price of a system is very high.
However, in recent years, this situation has begun to turn, and the three major problems are gradually being resolved.
In terms of technology, the maturity of deep learning algorithms has greatly improved the accuracy of face. Advances in computer technology based on deep learning algorithms provide powerful computational and analytical tools for face recognition. In turn, the huge amount of biometric data also provides a wealth of material for machine training, "big data becomes the fuel of artificial intelligence." Face recognition, Face++ team created the world's highest face recognition accuracy rate, has been in the face detection FDDB evaluation, face key point positioning 300-W evaluation and face recognition LFW evaluation, won three consecutive World number one.
In terms of applications, at present, the application scenarios of biometrics have been greatly expanded. The bank applies biometric identification in the customer identity verification scenario, covering different risk level scenarios such as weak real-name electronic account opening, settlement account opening and deposit and withdrawal. Mobile payment applications such as third-party payments and mobile banking have begun to use biometrics. The public security department has introduced face recognition technology in video surveillance and personnel access management in many types of places. Under the policy promotion, face recognition has also been placed in the social security, education, and medical systems. In recent years, domestic smart phone consumption has increased dramatically, mobile users' mobile payment habits have gradually developed, and smart homes have become increasingly popular. The growth of personal consumption demand in three aspects has pushed consumer-side face recognition on mobile phones to break out.
In terms of price, face recognition equipment has been declining in the past two years. In recent years, advances in technology and improvements in algorithms have made face recognition technology a new step. With the promotion of the national government and policy support, China's face recognition technology and applications have made great progress.
There are currently 3 mainstream solutions for 3D sensing: structured light, time of flight (TOF) and binocular ranging:
Structured Light: The structured light projects specific optical information onto the surface of the object and is captured by the camera. After the spots are struck on the object, the position captured by the camera is different because of the distance from the camera. The information such as the position and depth of the object is calculated according to the change of the optical signal caused by the object, thereby restoring the entire three-dimensional space. Apple iPhone X face recognition technology uses 3D structured light technology.
TOF (TIme Of Flight): Captures near-infrared light from transmission to connection through a proprietary sensor.
The flight time is collected to determine the distance of the object. The hardware implementation of TOF is similar to that of structured light. The only difference is that in the algorithm, the structured light is projected using the encoded optical information, and the TOF directly calculates the phase difference between the light and the pixels. This technology was used by Microsoft on the second generation of Kinct.
Stereo System: The principle is similar to human eyes. Under natural light, the image is captured by two cameras. The triangle principle is used to calculate and obtain depth information. The current dual camera is a typical application of binocular ranging. It is less used on mobile devices and is used in outdoor robots.
Since binocular ranging technology has many limited conditions and cannot be used in the night, commercial 3D depth vision includes both flight time and structured light.
Benefiting from the gradual solution of the three technical difficulties, the domestic face recognition industry is welcoming unprecedented development opportunities, and its application scope and market scale are expected to achieve rapid expansion. The combination of near-infrared face recognition and visible light technology, the development of 3D structured light and depth camera have brought technological innovation to face recognition technology. The breakthrough of deep learning algorithm greatly promotes the development of artificial intelligence and face recognition technology, and improves the efficiency and accuracy of recognition.
Consumer applications are about to break out, face recognition scenes are becoming more diverse
With the advent of the era of brushing, the market for face recognition is broad, the profit model is changeable, and the industrialization of the consumer sector will break out.
Internet +: Face recognition technology has been widely used in the Internet field. Through the deep learning algorithm, Shangtang Technology realizes face detection and classification in Sina Weibo's “face album†function; it is regarded as a series of hardware and software products such as Mito's Mito Xiu Xiu App, Beauty Camera, Beauty Phone, etc. Provides face recognition technology support. Among them, Meitu Xiu Xiu and Mei Mei Camera App can accurately locate the face and facial features in the image through Face++'s face detection and key point detection technology, so as to perform portrait whitening, facial features and other treatments, and quickly complete the precision. Trimming.
New Retail & Payment: Face recognition technology is also used in new retail areas, driving the development and implementation of unmanned retail. In September 2017, KFC cooperated with Ant Financial Co., Ltd. in its first upgrade store, K PRO, using a face recognition system and other technologies. Consumers can smile through the face recognition system. There is no set-top dining table and cashier in the store. Consumers can order food from the self-service ordering machine at the door. They can also use the mobile phone to scan the two-dimensional code on the table to order and pay. In the future, face recognition technology can also be used for passenger flow statistics, consumer psychology and behavior analysis. Through passenger flow statistics, the analysis of passenger flow and customer retention time in different regions and channels, combined with the sales performance report, can analyze customer purchase behavior and customer gender age composition.
Smartphone: Face detection and classification technology has been applied to smartphone applications, such as OPPO, Xiaomi and other mobile phones. The face clustering function of Shang Tang is applied, and cloud storage photos will be automatically classified, avoiding manual classification. The cumbersome operation of the photo optimizes the user experience.
In 2017, smartphone makers such as Apple, Xiaomi and Vivo have invariably matched the face unlocking function in the new models. In addition to the unlocking function, Apple FaceID face recognition can also replace the previous TouchID fingerprint recognition functions, including authentication, payment and so on. In terms of security, according to Apple's official news, the probability of cracking Touch ID by the same fingerprint is 50,000, and the probability of encountering the same face to crack Face ID is one millionth, and the security is improved by 20 times. . The layout of face recognition by many mobile phone manufacturers is expected to ignite the explosion of face recognition consumer level.
All in all, in addition to government, security, public security, and finance, face recognition has been gradually introduced in many fields such as the Internet, consumer electronics, automotive electronics, retail, medical care, and education. Face recognition is gradually infiltrating into all aspects of the consumer sector.
Face recognition is a group of heroes, giants and startups who can win?
Companies currently engaged in face recognition technology include three categories: industrial giants, Internet giants and startups, all of which have begun to lay out. Internet giants such as Google, Facebook, and Baidu are all in different areas of the face recognition industry chain. Traditional vertical vendors such as Apple, Haikang, Dahua, and Huawei are developing technologies to consolidate their original businesses. The way the giants develop is in extension and endogenous parallelism, such as Facebook's acquisition of Face.com and Apple's acquisition of PrimeSense. At the same time, we can also see that there are scorn, business soup, Yitu, Yun Cong and other startup companies have grown into unicorns.
From the perspective of terminal manufacturers, Apple, Samsung, Huawei, Facebook, Google's many patents show that the major terminal giants are deploying face recognition technology. The various technology giants mainly adopt self-research and development strategies supplemented by mergers and acquisitions. For example, Apple, Facebook, etc. are outsourced. Apple's face recognition application focuses on the mobile phone side, and has acquired face recognition related technology companies such as PolarRose, PrimeSense, PercepTIo, Faceshift, EmoTIent, and Turi; Facebook acquired Israel's face recognition company Face.com in 2012. The following table summarizes the outreach of foreign giant companies in the field of face recognition and its upstream and downstream in recent years.
In recent years, in the field of face recognition and its upstream and downstream mergers and acquisitions
In terms of self-developed technology, Google won the patent for face recognition unlocking mobile phones in 2012; Apple obtained the corresponding patent in 2015. In the domestic market, BAT is also struggling to compete in the field of artificial intelligence. In terms of capital, the application of deep learning algorithms to face recognition is currently at the forefront of Internet companies such as Baidu. At the same time, many entrepreneurial companies are not weak in technology, such as Sense TIme, Vision Technology, Etu Technology, and Yun Cong Technology. These companies are relatively mature both in terms of technology and application capabilities, and have gained great attention from the capital market. The following is a comparison of the technology and application scenarios of the four family face recognition unicorn company.
Four unicorn company core customers
Face recognition has become a trend in many fields. The layout of the four unicorn companies has also been focused on. They each rely on advanced technology to carry out in-depth layout in their respective fields of expertise and accumulate a wealth of customer resources. Shangtang Technology focuses on the fields of finance, security, mobile internet and mobile phones; Yitu Technology focuses on the fields of finance, security, medical and transportation; defiance of technology focuses on finance, security, retail, travel and other fields; cloud from science and technology focuses on finance , security, hotels, and other areas of innovation.
The profit model is realized from traditional hardware sales, software volume or on-time charging (SaaS mode/PaaS mode), software technical support, software and hardware integration solution, and big data realization that may be realized in the future. When an enterprise is able to deposit a large amount of high-quality data in a scenario and has the ability to exploit the value of that data, it has the ability to realize data. The Google search engine is an example of this. Images and videos are more massive than text, and companies with data sources in the future will have good business prospects in terms of data realisation. Artificial intelligence data sources will also be hot spots.
Strain/flex Reliefs And Grommets
the power Connectors we provide overmolding solutions and modular tooling.
We also offer to the OEM and distributor users a diversified line of strain / flex reliefs and grommets, such as Solid, Solid-Rib, Uniflex, Multiflex, in standard off the shelf or custom designs.
Overmolding the power connectors offers significant opportunities for cable improvements with higher pull strength not available with conventional backshells. Our technical staff is ready to help you from design and prototyping to small production run, assistance, and training.
Our team is ready to help with any of the following power connectors projects: overmolding mini fit jr. and mini-fit sr. connectors, , overmolded cables with micro fit terminations, sabre molded cable asemblies, amp duac overmolded power connectors, mate-n-lock power cables, power connector overmolding services, power connector molding, design and prototype of power cables across the board, small run molded power connecotrs , molded cable manufacturing, overmolding connectors for any power applications
Strain Reliefs And Grommets,Flex Reliefs And Grommets,Cable Strain Reliefs,Cable Flex Reliefs,Cable Grommets,Molded Strain Relief
ETOP WIREHARNESS LIMITED , https://www.oemmoldedcables.com