Features of access control Face Recognition Time Attendance system

1) Modular system structure function.

The Access Control Face Recognition Time Attendance system adopts a "server + workstation" modular structure, which is convenient for different intelligent departments to carry out independent management according to their authority, and avoids the phenomenon of authority crossing and management confusion. For example, the data server is specially used for data exchange and storage; the maintenance workstation is used for the maintenance of the access control Face Recognition Time Attendance system; the application workstation is used for the management of passengers and employees;

2) Powerful offline use function
The pixel face recognition time attendance access control management system has a powerful offline function. When the hardware system and the Access Control System cannot communicate normally, the access control system can still be used normally without affecting the entry and exit of passengers. The storage capacity of the hardware controller can reach up to 100,000 photos and 100,000 historical records, which will not cause data loss due to short-term communication failures.
3) Powerful linkage function
The access control Face Recognition Time Attendance system has a powerful linkage function, which can seamlessly link with Face Recognition Time Attendance, voice broadcast and other equipment. When the system triggers an alarm, it will automatically save on-site photos and voice broadcast for voice prompts. For example, when an illegal card is used to enter the channel, the access control face recognition and attendance system automatically performs image capture and voice prompts, and at the same time links sound and light alarms.
4) Deep learning Face Recognition Time Attendance based on face big data, which greatly improves system robustness and recognition accuracy.
The Face Recognition Time Attendance algorithm adopts the deep learning mode based on neural network. By using a large number of simple processing units interconnected to form a complex access control face recognition and attendance system, it imitates the human learning and cognitive system, and obtains the implicit expression of the rules and rules of face recognition and attendance that are difficult to achieve in the process of learning. Through the use of shape features, grayscale features, skin texture features and other traditional features and fusion, using spatial analysis and scheduling learning technology to achieve high performance, high precision, high robustness, reliable face comparison algorithm;
Based on practical applications in security, public security, education, finance and other industries, we already have hundreds of millions of face big data of different quality, posture, light, gender, etc. for deep learning, using massive data, using deep learning, automatic learning to get human facial features. After being trained with a large number of face positive and negative sample data, the algorithm has obvious advantages in accuracy, fault tolerance, robustness, etc. It has been tested by many large-scale projects and fully meets the actual application.
5) Provide powerful face image preprocessing tools
In the process of building the follow list database, the quality of the photos is uneven, and the photo images can be automatically or manually processed to make them meet the relevant standards and requirements. Face photo processing functions are as follows:
It supports artificial cropping of face photos through plug-ins or other methods.
Support calling third-party image processing and analysis tools.
Image processing tools include: color processing, brightness adjustment, contrast adjustment, saturation adjustment, sharpness adjustment, color adjustment, fisheye correction, light balance, eraser, background cleaning, original image restoration, cropping tools, super resolution, automatic Multi-level brightness, automatic multi-level contrast, automatic multi-level saturation, automatic multi-level sharpness, etc.