As face recognition technology gains momentum, the second half of fingerprint recognition is gradually losing its shine. Many smartphone manufacturers are now focusing on developing facial recognition systems, despite current limitations in user experience and technical maturity. However, the pursuit of new technologies continues, and with the rise of face recognition, fingerprint recognition faces two major challenges.
Since the introduction of Face ID on the iPhone X, facial recognition has become a hot trend. From the later releases of vivo, OPPO, and Huaqin, to future models from Huawei and Xiaomi, all have either adopted or are planning to adopt face recognition. This shift has caused fingerprint recognition, which once dominated the market, to enter a period of stagnation.
[Image: "Face Recognition and Fingerprint Recognition Develop Together – Why Is Fingerprint Recognition Falling Behind?" (500x275)]
Although fingerprint recognition remains the best option for full-screen devices, the development of optical or ultrasonic under-screen fingerprint technology has not yet reached a commercial stage. As a result, the iPhone X opted for 3D face recognition instead. Meanwhile, companies like vivo, OPPO, and Huaqin are currently using both face recognition and capacitive fingerprint solutions.
From this perspective, the competitiveness of fingerprint recognition is slowly declining. However, user feedback on the iPhone X shows that Face ID isn’t as fast or convenient as traditional fingerprint scanning. Domestic manufacturers' face recognition features are often seen more as a novelty than a necessity. At a press conference, Luo Yonghao from Huaqin even stated that face recognition is less secure than fingerprint recognition, advising users to rely mainly on fingerprint authentication.
So, how can capacitive fingerprint recognition remain competitive when face recognition hasn't reached a satisfactory level, and under-screen fingerprint tech is still not widely adopted?
Professor Guo Zhenhua from the Shenzhen Graduate School of Tsinghua University suggests two research directions: first, applying deep learning to improve fingerprint recognition accuracy; second, collecting biometric data to enhance anti-counterfeiting capabilities.
Currently, fingerprint recognition faces two key challenges. First, its performance under large-scale population conditions is limited. Some users have unclear or difficult-to-identify fingerprints, and while one-to-one recognition is accurate, the accuracy drops significantly as the number of users increases. Second, the anti-counterfeiting ability of fingerprints is not strong enough. There have been numerous reports of fake fingerprints being used through films, photos, or broken fingers.
To address these issues, Guo Zhenhua proposed solutions. He suggested using deep learning to enhance recognition accuracy. Unlike traditional methods that hit a performance bottleneck with large datasets, deep learning improves continuously as more data is processed, leading to better results.
For anti-counterfeiting, he introduced Optical Coherence Tomography (OCT), a medical imaging technique used for diagnosing eye and cardiovascular diseases. OCT uses near-infrared light and optical interference to create detailed images of biological tissues, capturing both surface and internal fingerprint structures. Fake fingerprints lack sweat glands and internal details, making them easier to detect.
Additionally, Optical Angularography (OAG) can be used to process OCT data, distinguishing between static and dynamic scattering particles. Using milk flow in a pipeline as an example, higher flow rates increase the dynamic signal strength. Similarly, this technique helps identify living fingerprints by detecting blood flow information.
Although fingerprint recognition may appear less prominent compared to face recognition, it has long been a standard feature in smartphones. It will continue to dominate the low-end market and see steady growth. With the integration of deep learning and live detection technology, fingerprint recognition becomes more accurate and secure, increasing the difficulty and cost of attacks. While the future of fingerprint recognition remains uncertain, it still holds significant potential in competing with other biometric technologies like face recognition.
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