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With growing concerns over public safety, face recognition technology has rapidly evolved into a key standard in the security industry, offering vast potential for future development. As more companies invest in this field, face recognition is becoming a major trend in the market. Several listed firms have recently launched products or announced research initiatives related to facial recognition. For instance, Gao Xinxing has already developed a face recognition system designed for financial security, which can be integrated into ATMs. Meanwhile, Chu Junzhi Zhisheng Wu Junjie stated that their products will soon hit the market, focusing on surveillance and security applications.
Saiwei Intelligence, Guoteng Electronics, and Hanwang Technology have also revealed technologies that could be applied to face recognition through investor communication platforms. According to industry estimates, face recognition products in mainland China are expected to generate over 10 billion yuan in annual sales within the next three years. Analysts suggest that while the technology itself is not new, its integration with security systems is what will drive real growth in the sector.
Currently, most video surveillance data relies heavily on manual review, which is inefficient and time-consuming. The challenge of processing unstructured video data is a significant bottleneck for the security industry. A report by the Forward-looking Industry Research Institute highlights that the implementation of face recognition can significantly enhance the efficiency of back-end video processing, improving overall system performance.
In response to this demand, many companies have introduced video snapshot search methods, using “map-based†techniques to filter large volumes of video content. Leading domestic security firms like Hikvision and Dahua are actively developing backend software platforms, while international players such as Samsung and HP also offer intelligent retrieval solutions. As smart cities continue to expand, the market for face recognition technology is expected to grow even further.
Despite its promise, face recognition still faces several challenges in real-world applications. One major issue is age variation—public security departments often encounter discrepancies between current and past photos, making accurate identification difficult. Posture changes also affect accuracy, especially when recognizing active faces. While current systems can achieve high recognition rates at ±10 degrees, improvements are still needed.
Additionally, the diversity of image sources—such as digital cameras, scanners, and mobile phones—can impact recognition accuracy due to differences in imaging mechanisms. However, ongoing advancements in the technology are gradually addressing these issues.
In terms of speed, two main approaches are used: file-based and database-based recognition. File-based comparison can process up to 200,000 people per second, while database-based systems can handle around 10,000 people per server. Researchers are working to combine the benefits of both methods, aiming for faster and more accurate results. For active face recognition, systems must operate efficiently, with detection times under 40ms and full processing from detection to recognition completed in less than 2 seconds for a database of 100,000 individuals. This level of performance is sufficient for most practical applications.
Face recognition as a new look at the market