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Biometric Recognition Technology Application Program in the Financial Industry

Author: huifan   Time: 2021-05-24

This article introduces a sub-branch of the professional direction of pattern recognition-biometric recognition technology and its application in the financial industry, and analyzes and compares it.

HFSecurity ATM Biometric recognition application

1. Introduction

With the development of modern pattern recognition technology, some specific branching technologies in pattern recognition technology have gradually matured, and many of them have reached the level of application. Of course, most of these applications are still based on limited environmental conditions. Under the application. 


2. Classification of biometric technology


The so-called biometric technology is: through the close combination of computers and high-tech means such as optics, acoustics, biosensors and biostatistics, the use of the inherent physiological characteristics of the human body (such as fingerprints, facial images, iris, etc.) and behavioral characteristics (such as handwriting) , Voice, gait, etc.) for personal identification. Traditional identification methods use identification items (such as keys, certificates, ATM cards, etc.) and identification knowledge (such as user names and passwords), but because they mainly rely on external objects, once the identification items and identification knowledge that prove the identity are stolen or forgotten , Its identity can easily be impersonated or replaced by others.


 Biometric Recognition Technology Application Program in the Financial Industry


Biometrics is more secure, confidential and convenient than traditional identification methods. Biometric identification technology has the advantages of not easy to forget, good anti-counterfeiting performance, not easy to forge or stolen, "carry" with you, and available anytime, anywhere. Biometrics technology can be widely used in government, military, bank, social welfare, e-commerce, security and defense. For example, a depositor walks into a bank, and he does not bring a bank card, nor does he recall the password. When he withdraws at the cash machine, a camera scans the user’s eyes and then quickly and accurately Locally completed the user identification and handled the business. This is a real scene that took place in a sales department of the United Bank of Texas in the United States. The business department uses the "iris recognition system" in modern biometric technology. The relevant technologies currently being studied at home and abroad can be divided into the following seven specific technologies: face recognition, iris recognition, fingerprint recognition, palm Pattern recognition, vein recognition, voice recognition, handwriting recognition, behavior recognition, video supervision.


The first six of these seven technologies belong to the category of identity authentication, which is to verify who the person or object is. Another branch derived from behavior recognition is video surveillance, which is an auxiliary means of identity verification. However, it has been discovered that it has greater use beyond identity verification, so it has gradually been independent.


2.1 Face Recognition


Face recognition is a technology for identifying each person's identity based on the inherent physiological characteristics of a person's face. It belongs to a kind of biometric technology. It has the characteristics of strong intuition and good posteriority. However, it is limited to the large amount of information on facial features and the changes in facial features brought about by growing individuals. This brings challenges to the accuracy of this technology.


The research of face recognition technology started in the late 1960s. Since the late 1990s, some commercial face recognition systems have gradually entered the market. However, these technologies and systems have a certain distance from practicality, and their performance and accuracy need to be improved. After the United States suffered a terrorist attack, this technology has attracted widespread attention. As the most easily concealed recognition technology, face recognition has become one of the most important means of international anti-terrorism and security precautions.


The factors that affect the performance of face recognition are as follows:

  • 1. Background and hair: Eliminate the background and hair, and only recognize the part of the face image.
  • 2. The translation, scaling, and rotation of the face in the image plane: Geometric normalization is adopted. After the face image is rotated, translated, and scaled, the final face image is of the specified size, the eyes are level, and the distance between the eyes is constant.
  • 3. Deflection and pitching of the face outside the image plane: A three-dimensional model of the face can be established, or three-dimensional fusion (morphing) can be performed to restore the face image to a frontal image.
  • 4. The change of the position and intensity of the light source: the normalization of the histogram can eliminate the influence of part of the light. Using symmetric shape from shading technology, an image that is independent of the position of the light source can be obtained. [nextpage]
  • 5. Age change: establish an aging model of face images.
  • 6. Changes in facial expressions: extract features that are not sensitive to changes in facial expressions, or segment the face image into images of various organs, identify them separately, and then make comprehensive judgments.
  • 7. The influence of attachments (glasses, beards).
  • 8. The change of photo (photo) camera: The images taken by the same person using different photo (photo) cameras are different.


The main performance indicators for measuring face recognition are: 1. False AcceptRate: This is the probability of mistaking other people as designated personnel; 2. Rejection rate (FalseRejectRate): This is mistaking designated personnel as other personnel Probability.


The two indicators are also different when the threshold used by the computer is different. In general, the false recognition rate FAR increases with the increase of the threshold (relaxed conditions), and the rejection rate FRR decreases with the increase of the threshold. Therefore, the error rate (EqualErrorRate; ERR) can be used as the performance indicator. This is the adjustment threshold to make the FAR or FRR when the two indicators of FAR and FRR are equal. Face recognition has one of the most obvious features compared to other biometric recognition technologies. The advantage is that the posterior is easy. Basically, it is possible to verify whether there is an error in the identity authentication of this person by judging by the human eye, but other technologies are impossible to judge by such a simple method, and basically require the cooperation of experts and special equipment.


2.2 Iris recognition

According to clinical medical observations, the iris is located behind the cornea and in front of the lens. The iris has a unique structure, and its color varies with the amount and distribution of pigments, and this unique iris structure has good stability. At present, the mainstream iris recognition system application is: the system uses monochrome TV and video method combining camera technology and software to obtain iris digital information, and compare the scanned information with the pre-stored template information during verification to make Identification.


Advantages of iris recognition technology:

1. The collection of biological characteristics is more convenient.

2. High accuracy: According to statistics, the error rate of iris recognition is the lowest among all kinds of biometric recognition so far.


Disadvantages of iris recognition technology:

1. The application popularization program is low: Many tests have been carried out for the iris recognition system, but there is no larger-scale application.

2. High cost: It is difficult to miniaturize the size of the image acquisition device; at the same time, an expensive camera is required.


In the research of iris recognition technology, the research technology of the State Key Laboratory of Pattern Recognition of the Institute of Automation of the Chinese Academy of Sciences is at the international leading level. They not only have an international leading position in software algorithms, but also can design and manufacture iris recognition equipment by themselves. This is in the world The field of iris recognition is unique. They even made a handheld miniature iris collection device in 2004, and at the same time greatly reduced the hardware cost of the iris device. In 2006, they also made a big breakthrough in the research of long-distance (more than three meters) iris collection equipment. If this research is successful, iris recognition can be used for long-distance and interference-free collection, without the need for the current high Accurate alignment is required.


2.3 Fingerprint Recognition

Fingerprint  scanner recognition refers to the uneven lines on the skin on the front of the finger end. It is medically proven that these lines are different in patterns, break points and intersections, and are unique and permanent. At present, the mainstream fingerprint recognition system application is: the user puts a single finger on the prism surface or glass plate, and scans through the CCD sensor device. The obtained fingerprint image is digitized, processed and analyzed, and finally extracted as acceptable fingerprint digital feature information, and then stored on the memory or on the card as a reference template. When used, the information scanned by the fingerprint reader and the template information are used. Compare and make identification.


Advantages of fingerprint recognition technology:

1. Strong practicability: fingerprint samples are easy to obtain and easy to develop identification systems.

2. Reliability is easy to increase: the accuracy can be improved by registering more fingerprints and identifying more fingers.

3. Good convenience: the speed of scanning fingerprints is very fast and it is very convenient to use;

4. Wide application: Fingerprint recognition technology has occupied most of the market.

5. Fingerprint identification products have better cost performance: the fingerprint collection head is more miniaturized and the price is low. [nextpage]

 HFSecurity financial biometric recognition device

Disadvantages of fingerprint recognition technology:

1. The versatility of fingerprints is poor: because the fingerprints of individual people or certain groups have few fingerprint features, imaging is difficult, which has a certain impact on the application of this technology.

2. Poor user acceptance: In the past, the use of fingerprints in criminal records caused certain psychological barriers to users.


2.4 Palmprint Recognition

The palm geometry is based on the fact that almost everyone's hand shape is different, and the shape of this hand no longer changes significantly after a person reaches a certain age. When the user puts his hand on the hand-shaped reader, a three-dimensional image of a hand is captured, and then the shape and length of the fingers and knuckles can be measured and compared.


According to different data used to identify people, hand shape reading technology can be divided into the following three categories: the pattern of blood vessels in the hand, and the geometric analysis of the palm and fingers. Mapping the different characteristics of the shot is quite simple and does not generate a large number of data sets. However, even with a considerable number of records, palm geometry may not be able to distinguish people, because the characteristics of the hands are very similar. Compared with other biometric methods, palm geometry cannot achieve the highest degree of accuracy. As the database continues to grow, it is also necessary to increase the number of distinct features of hands to clearly identify and compare people and templates.


2.5 Vein Recognition

Vein recognition is a new technology that has only emerged in the last two years. In this technology, the main research is the vein flow distribution map near the human hand for identification and identification. It generally uses an infrared CCD camera to collect the vein map. When the recognition system obtains the image of the vein on the back of the hand through the infrared CCD camera, the feature code of each vein image will be stored. Then, the user's vein map and the stored vein map feature code comparison and feedback results are realized.


Features of Vein Recognition System:

1. The vein recognition system depends on the state of the back of the hand.

2. Even if the back of the hand is slightly changed, the vein recognition system recognition will be affected.

3. If the user has arthritis or rheumatism, the vein recognition system cannot be used.

4. Contact with this system can maximize your comfort and convenience. This system is superior to other biometric systems in terms of humidity, sweat, dirt, pen marks and small injuries.


Because its technology is very close to fingerprint recognition, the scope of use and application environment are also similar, so many fingerprint recognition companies use it as a new alternative update technology for promotion and application.


2.6 Voice recognition

The physical, psychological, and behavioral characteristics of human voice parameters will be reflected in the human voice waveform. The human voice spectrum includes the time change of the curve and the characteristics of the driving sound source are different. It is possible to extract features that have great changes in the voice of different people and small changes in the voice of the same person for analysis, comparison, and recognition. At present, the mainstream voice recognition system applications are: recording human voices through microphones, digital signal processor digitization and software compression, extracting voice and image information and storing it in the database, and applying the instantaneous collected voice and characteristic information in the database. Match and make identification.


Advantages of voice recognition technology:

1. Voice recognition is a non-contact recognition technology that users can accept naturally.

2. Voice recognition technology has better convenience, economy and accuracy.


Disadvantages of voice recognition technology:

1. The accuracy is low: because the range of sound changes is too large, it is difficult to accurately match.

2. The technical complexity is high: the volume, speed and sound quality of the sound will be affected by certain conditions (such as a cold), and system functions need to be increased to adapt to this change.

3. Higher cost: sound collection equipment (such as high-fidelity microphones) is very expensive. [nextpage]


2.7 Handwriting recognitionis

Handwriting recognitionis like the person. Chinese people pay attention to calligraphy. After people choose their favorite calligraphy style, they integrate their own writing characteristics. Therefore, it can be as small as the structure of a word, as large as the vertical and horizontal layout of the entire article, everyone All have their own pen-handling habits and format planning; handwriting has become one of the important means for people to identify identity. At present, the mainstream handwriting (signature) recognition system application is: the system uses a wired pen and a sensitive graphics tablet to extract the dynamic process information characteristics of the signature, and distinguishes the habitual part of the signature and the change part that changes almost every time the signature is signed. Information features to determine the true identity of the signatory.


Advantages of handwriting recognition technology: better acceptance: the use of handwriting recognition is a recognized technology for identification. Easy to accept by the general public.


Disadvantages of handwriting recognition technology: 1. High technical complexity: as people's temperament and lifestyle change, signatures will also change, and system functions need to be increased to adapt to this change. 2. High price: The handwriting board used for signature is complicated and expensive.


2.8 Behavior Recognition

Behavior recognition technology is a video analysis system that monitors, classifies, tracks, and counts objects. Behavior recognition technology analyzes and judges based on certain rules, so that alarms for specific behaviors can be set.


Behavior recognition is a video technology based on certain patented technologies, which are introduced as follows:

1. Intelligent video recognition: a family of video image algorithms that can be used to detect and compensate for a series of changes caused by the environment and cameras: camera stability, background recognition, perspective accuracy, adaptive limits, shadow ignorance, PTZ camera control .

2. Target segmentation: The engine can accurately segment the target object from the background, ignoring the changes of non-target objects, such as the movement of trees and changes in light. The target group can also be divided into individual targets.

3. Trajectory tracking: When the target is detected over a certain time limit, the target's action, trajectory and speed function are established, so that the size, quantity and shape of the object can be determined more accurately. The trajectory of the displayed target is updated in real time to identify the path of the intruder's intrusion direction.

4. Target detection: It is mainly to judge the position, size and shape of the target and high-precision filtering of non-target objects.

5. Behavior recognition: apply certain rules to recognize the position, speed and direction of the target; in addition, the number of targets can also be judged.

6. Efficient development tools: Developed a parallel operation mode and a multimedia instruction set of advanced central processing unit, giving the industry's highest cost performance.


2.9 Video supervision

Video surveillance is called intelligent video surveillance technology in traditional terms.


In addition to basic technologies such as moving target detection, the application of intelligent video surveillance technology must also be combined with other algorithms and technologies. In intelligent video analysis, image segmentation and moving target detection are basic problems. In recent years, many researches have been done on these problems, but they are still challenging topics. The core technical problems that need to be solved are motion blur and background difference methods. Changes in light, real-time requirements, occlusion issues, etc.


Early lens segmentation algorithms were carried out in the pixel domain, but this method is very sensitive to the rapid movement of pixels, leading to a large number of false detections. Later, a lens segmentation algorithm based on the histogram difference between frames was developed. Because of its low algorithm complexity and good lens segmentation effect, it has become a popular method at present.


Moving target detection and tracking are the basis of automatic or semi-automatic video surveillance high-level applications such as event detection, behavior recognition, video image compression coding and semantic indexing. At present, the methods of moving target detection include: temporal differencing, background subtraction, and optical flow-based methods.


The background difference method is a gray-based detection method for moving targets, and the feature-based method is commonly used. Feature-based detection is based on image features (points, lines, moments) or a model composed of features (polygons, polyhedrons) to detect moving targets. It is mostly used when the target is large, the feature is easy to extract, or there is a standard target model library for matching. The background difference method can extract a very complete target, but it is susceptible to background changes caused by illumination. In recent years, some statistical methods have been introduced to implement background modeling and background elimination, which greatly enhances the robustness of the background difference method to noises such as light changes and shadows. There are many methods for background modeling based on model characteristics. Among them, statistical model modeling based on pixel intensity can adapt to gradual lighting, but there are problems with sudden lighting. Kalman filter as a simple and easy method has been widely used in target tracking.


Texture analysis is based on optical flow field and motion parameter estimation. It is another commonly used motion area detection algorithm. However, due to aperture and occlusion issues, the reliability of optical flow is relatively poor. The motion segmentation method based on Bayesian probability statistics can perform segmentation and motion estimation at the same time, and the effect is good, but the calculation is complicated, the calculation amount is large, and it is not suitable for real-time processing.


At present, there are mainly two types of digital video surveillance systems at home and abroad, one is a video surveillance system with digital video equipment as the core, and the other is an embedded intelligent video surveillance system. The embedded video surveillance system is a dedicated computer system that is application-centric and adapts to the application system's comprehensive and strict requirements for function, reliability, cost, and volume. The digital signal processor (DSP) is a dedicated processor for high-speed real-time processing of digital signals. Its processing speed is 10-50 times faster than the fastest CPU. It has been widely used in video surveillance systems. Front-end integration, video digitization, monitoring networkization, and system integration are the development directions of intelligent video surveillance systems. Digitization and networking are the main characteristics of the development of intelligent video surveillance.


3. The application status of biometric recognition technology


Analyzing the characteristics of biometric recognition technology, we can see that almost all the application objects of biometric recognition technology are similar, and the application environment is only slightly different. However, due to the differences between technologies and the development of different technologies, different technologies have their own specific application scenarios. This can be divided into the following aspects: 1. Transcendental defense authentication: used for access control, channel management, bank withdrawal ATM machines, etc.; 2. Posterior analysis and identification: after the incident occurred based on the characteristics obtained on the spot Information analysis and verification methods, such as police fingerprints and DNA collection and analysis at the scene of the crime; 3. On-site behavior analysis: analyze the behavior of moving objects and the attributes of surrounding objects to obtain the judgment and analysis results of the behavior of the objects. Such as the analysis of road traffic accidents, the judgment of vehicles running red lights and illegally changing lanes, and reporting to police in the restricted area of ​​prisons.


3.1 Bank ATM application analysis

In view of the application of biometric recognition technology in the financial industry, Beijing has prepared to apply facial recognition analysis on bank ATMs. Let's start with face recognition technology.


ATM adopts face recognition technology. Generally, the process is divided into two application forms: Face recognition is used as an identity authentication method (belonging to a priori defensive authentication): a person approaches the ATM device to insert a card, and the camera takes a picture of the face during the password input. The front photo, the photo is sent back to the server for comparison and verification, and the information is sent back to the ATM, and then the ATM system simultaneously performs a password judgment to determine whether to perform subsequent operations. Use face as a data collection method (belonging to a posteriori analysis and identification): a person approaches the ATM device to insert a card, the camera takes a photo of the front face of the face while the password is input, and the photo is sent back to the server for storage, and the ATM system performs a normal password judgment after the password is judged. operating.


 Under normal circumstances, as a technician who has developed face recognition technology and has an understanding of related technologies and equipment at home and abroad, we recommend that face recognition is used for authentication of relevant financial ATM equipment as a data collection method. It is the second form above. This is because of the characteristics of the application environment of ATM equipment. The characteristics of the application environment of ATM equipment are as follows: 1. There are a large number of users. Because the current face recognition technology has rarely conducted comparison experiments with large amounts of real face data, and the current data experiments are all collected in a laboratory environment. , The collected surrounding environment is relatively stable, and the angle and degree of cooperation of the human face are relatively high, so it cannot be used as a reference for the actual data of the existing actual environment application.


2. Most of them work outdoors and in a small number of blocked positions. Face recognition technology has relatively harsh requirements for light, especially for face recognition technology under visible light. The difference of a single light source, such as light source direction, size, shape, color temperature, distance, light intensity, etc., can make the collected face images have many different differences, making the face appear as one person, making the recognition system appear Misunderstanding. The outdoor light conditions are too complicated, especially the solar spectrum will cover all visible light and invisible light, so the research of outdoor face recognition equipment has been in an impossible state.


3.2 Analysis of the characteristics of biometric recognition technology

The use of other identification technologies can only rely entirely on the judgment of the identification device itself, and it is impossible to make direct judgments by ordinary banking or financial industry practitioners.


Here we can list some of the problems that may occur in related technologies:

  • 1. Iris recognition: The most accurate biometric recognition technology, but requires higher alignment accuracy. In addition, when people are sick and take medicine, the iris will change within a certain period of time due to the action of the medicine.
  • 2. Fingerprint recognition: Fingers due to oil stains or their own secretions may cause problems that cannot be recognized by the fingerprint recognition device.
  • 3. Palmprint recognition: The palm has a large area. Although the accuracy is much higher than that of fingerprints, and even claims to be close to iris recognition, there will be problems similar to fingerprints. Its equipment is also much larger than fingerprint recognition equipment.
  • 4. Vein recognition: Even if the back of the hand is slightly changed, the vein recognition system recognition will be affected. If the user has arthritis or rheumatism, the vein recognition system cannot be used.
  • 5. Voice recognition: Illness, especially throat disease, will cause changes in the vocal cords and make it impossible to recognize. At the same time, it is easy to be fooled by recording on tapes and other recording devices.
  • 6. Handwriting recognition: It will change because of people's mood and age. After short-term special training, it will also change people's handwriting habits.
  • 7. Behavior recognition: At present, the recognition technology with the lowest recognition accuracy rate has not reached the level of laboratory use due to too many factors, let alone the level of commercial use.


3.3 Intelligent video surveillance technology

Above we have analyzed the application classification of biometrics in authentication technology. Here we are going to analyze the auxiliary authentication method of intelligent video surveillance.


At present, mature intelligent video surveillance technology can perform some analysis on the behavior of objects. This behavior analysis is currently unable to analyze detailed behaviors such as fights, but the analysis of the overall behavior of objects can already be done, such as the behavior of objects. Form trajectory, color, shape, driving speed, speed change, volume.


Through the use of these analysis data, we can achieve the following functions:

  • 1. Restricted area alarm: use in bank vaults and some areas where people need to be restricted from entering. When someone enters this area without normal identity authentication, the system automatically alarms and saves the relevant image and video data as evidence or activates the relevant capture equipment for detailed capture and analysis.
  • 2. Direction control: Monitor the movement direction of all objects within the financial management center and the perimeter. During a specific time, such as night or a specific alert time, no one should enter or approach the security agency or related facilities. If someone approaches For these confidential institutions or related facilities, the system can automatically alarm and save relevant image and video data as evidence or activate relevant capture equipment for detailed capture and analysis.
  • 3. Quantity calculation: Intelligent video surveillance technology can calculate how many moving objects are in the coverage of the camera, which can limit the number of people entering the vault and other areas. When the number of people exceeds the limit, the access control system cannot be activated even if the authentication is passed. And open the door.

 HFSecurity face recognition finalcial application

The application of this technology can be used as an auxiliary means of authentication, and authentication equipment can also be used as an auxiliary means of this technology to realize security control in the whole area.


3.4 Possible applications in the financial industry

Analysis 3.4.1 Peripheral independent devices such as bank ATMs use face recognition technology for face capture and auxiliary authentication, but it should not be used as the only authentication method. Use intelligent video surveillance technology to obtain behaviors and object characteristics that may cause damage to ATMs and other equipment.


3.4.2 Confined spaces such as vaults

Face recognition technology is used for face capture and auxiliary authentication, but it should not be used as the only authentication method. You can consider co-authentication with other biometric recognition technologies, so as to avoid possible threats such as the loss of keys and RFID cards. The captured image of the face can be used as a posterior technology in the future.


Intelligent video surveillance technology is used to calculate the number of people near the door, which can be set by rules. For example, if there are more than two people within a few meters of the door, the access control system cannot be opened even if the authentication is passed.


3.4.3 Bank business hall

For the analysis of the facial image information obtained by the to-do business personnel in the process of entering the business hall to obtain the queuing number, some special priority services can be provided to some VIP customers, and the image can also be obtained at the same time when the person goes to the service window to handle the business. After comparing the two, you can get whether the person who handles the business is the same person as the person who took the number, and whether the two persons are the owner of the card, so as to avoid major losses to the customer due to the loss of the bank card.


4. Analysis of application schemes in the financial industry

Here we perform classification analysis according to the size of the technical control area, and basically divide the application scope of biometric recognition technology into point-based entrance and exit control and area-based area control.


4.1 Access control

In terms of access control, it mainly refers to access control, such as the vault door of a bank, the passage of important places, and other key points that require identity authentication. The entrance and exit control is divided into two aspects: general entrance and exit control system and important entrance and exit control system for system architecture analysis.


4.1.1 General access control

General entrance and exit control is the management of garage doors, park gates, general office doors or some less important entrances and exits. In these scenarios, other high-precision authentication technologies related to face recognition assistance (such as iris recognition or RFID cards) can be considered for identity authentication. At the same time, smart video surveillance technology and DVR equipment can be used to assist in recording the behavior of entering personnel to provide auxiliary authentication analysis. And the basic data for subsequent analysis.


4.1.2 Important entrance and exit control

Important entrances and exits are the management of the entrances and exits of important core institutions such as archives, treasury, financial office, and general manager's office. In this scenario, the most complete authentication and analysis system can be used: other high-precision authentication technologies (such as iris recognition or RFID cards) related to face recognition are used for identity authentication, and smart video surveillance technology is used to monitor the number of visitors. Controlled management with related information realizes the chain management of identity authentication and security alarms, and avoids accidents that occur due to coercion of personnel or intrusion of external personnel. It can be said that such an entrance and exit control system is basically impeccable.


4.2 Regional Security Management

All access control technologies are part of the security management of the entire region, and regional security management is an effective measure to ensure the safe and stable operation of relevant organizations in the financial industry without accidents. Here we will provide A set of regional safety management measures that we believe are effective.


For regional safety management, it is recommended to use building/community overall monitoring and entrance and exit control to achieve a regional overall intelligent control management system.


We use a variety of biometric recognition technologies to implement this system:

  • 1. For fences and abnormal passages, monitor through intelligent video surveillance technology, and record all abnormal conditions and report to the police in time;
  • 2. Adopt the two forms mentioned in section 4.1 for security defense for gates and entrances;
  • 3. For all objects entering the area, the camera fusion technology is used to track and record the whole process, and the movement trajectories and behaviors of all objects are marked. When an abnormal situation occurs, the source of the relevant deliberate object is immediately reverse-checked, so as to avoid The situation deteriorates further, while the suspicious objects are directly controlled;
  • 4. Each camera will have a local image and video storage space and a centralized storage space in the computer room;
  • 5. The intelligent video surveillance technology can greatly reduce the time length and space occupied by the video images stored by the DVR, effectively improve the utilization rate of the equipment, and make the subsequent analysis of the problem more pertinent and operability;
  • 6. The main weakness of the entire system lies in the power consumption. If the circuit is interrupted, the entire system cannot operate.


The above is the regional security control system that we think is more practical and feasible. The relevant technologies used in it are mature and can be directly put into practical use.

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