Face recognition attendance machine market grows rapidly

The traditional enterprise attendance system mainly includes manual attendance and credit card attendance. Manual attendance has a large workload and low work efficiency, while credit card attendance is a serious phenomenon in which others take over the card attendance, and the attendance card is often lost, and the attendance cost is high.

In the rapid development of social economy and the improvement of people's quality of life, people are eager for a more comfortable, convenient and intelligent way of working, which puts forward higher requirements in terms of office intelligence and convenience. With the constant standardization of the workplace, smart attendance has also become popular, which requires office attendance to have faster, more convenient and more accurate implementation. The traditional statistical attendance method of registering and signing employees by the administrative staff can no longer meet the needs of the modern workplace for quick and efficient. Nowadays, face features have been used as human identity tags to distinguish different individuals. With the continuous development and improvement of face recognition technology, it is very common to apply this technology to life. The combination of face recognition technology and attendance machine is also more common. The more widely it comes.

In recent years, the output of China's face recognition attendance machine industry has grown rapidly. By 2017, the domestic face recognition attendance machine industry output is about 717,000 units. The 420,000 units in 2016 increased by 70.7%:

China's face recognition attendance machine industry output in 2013-2018

In 2018, the demand for face recognition attendance machine in China is about 680,000 units, of which. The demand for high-end face recognition attendance machine is about 80,000 units, and the demand for low-end and mid-end is about 600,000 units.

China's face recognition attendance machine industry demand scale in 2013-2018

In 2018, the market size of China's face recognition industry was about 335 million yuan, an increase of 65.84% from 2.02 billion yuan in 2016.

Among them, the market size of high-end face recognition attendance machine increased from RMB 6.5 billion in 2016 to RMB 102 million, and the market size of low-end face recognition attendance machine increased from 1.37 in 2016 to RMB 233 million.

2018 National Face Recognition Attendance Machine Segment Size

From the current market point of view, China is still dominated by the low-end face recognition attendance machine. On the one hand, the low-end face recognition attendance machine has obvious cost advantages, and the company has a large purchasing willingness. On the other hand, face recognition The attendance machine has not been fully popularized, and the market space for high-end face recognition attendance machines has not been opened.

From the technical subdivision of face recognition attendance machine, the traditional face recognition technology is mainly based on face recognition of visible light image, which is also the most familiar recognition method, with more than 30 years of research and development history. However, this method has insurmountable defects, especially when the ambient light changes, the recognition effect will drop sharply and cannot meet the needs of the actual system.

In contrast, the near-infrared face recognition system is unique in that it completely solves the problem of ambient lighting that plagues the field of face recognition. The traditional visible light-based face recognition method will dramatically reduce the recognition effect when the illumination situation changes, which makes it impossible to use. Therefore, eliminating the ambient illumination is the first problem that must be solved in the practical face recognition system. The face recognition core technology and system based on near-infrared image can capture near-infrared face images that are not affected by ambient light changes under different lighting conditions, and the leading algorithm can achieve high recognition rate.