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Company News >> Screen Fingerprint or Face ID? Interpretation of the new trend of identification research
Since the appearance of FACE ID on iPhone X, Face Recognition has been used in real life, no matter the later released vivo, OPPO, hammer, or the future preparation of Huawei, millet, have adopted or ready to adopt face recognition . Menacing face seeing menacing, but before the development of fingerprinting in full swing has entered the dull period.
Although the best overall screen fingerprinting is the best choice, but the status quo is no matter under the optical screen fingerprint or ultrasonic screen fingerprint research and development have not yet officially entered the application period, therefore, Apple iPhone X 3D face recognition instead of fingerprint recognition, The vivo, OPPO, hammer is face recognition and capacitive fingerprint identification coexist, from the above point of view, fingerprint identification competitiveness is gradually dissipated.
However, according to iPhone X on sale for nearly three weeks, the user feedback situation, FACE ID is not as quick and easy as the fingerprint, and domestic mobile phone manufacturers carrying face recognition is not so much a way of identification, not to mention a curiosity. Hammer Luo Yonghao bluntly in his conference, the current face recognition security is not as good as fingerprinting, it is recommended that the user or the main use of fingerprinting.
So, in face recognition has not yet reached the level of application of satisfaction, and the screen is not yet formally applied under the window of the window, capacitive fingerprinting how to better participate in the new trend of mobile phone competition?
Guo Zhenhua, a graduate student at Tsinghua University in Shenzhen, thinks there are two main directions for research and development: one is to apply deep learning to fingerprinting and improve recognition accuracy; the other is to collect the physical components of a living body so as to better prevent fraud.
Judging from the current situation of fingerprinting, there are still two major challenges in the development of fingerprinting. On the one hand, the identification ability of large crowds is low. For example, some fingerprints of the user have unclear surface features and are difficult to identify. In addition, under the one-to-one conditions, the accuracy of fingerprints recognition is up to a certain extent, but in the case of an ever-increasing number, the recognition accuracy will be significantly reduced. Therefore, fingerprint recognition also urgently needs to improve the recognition accuracy.
On the other hand lies in the fingerprint security effect is less than ideal, fingerprints are fingerprints film, photos, broken fingers and other cracked news after another, therefore, fingerprint also urgently need security features.
To deal with these two major challenges, Guo Zhenhua also proposed a solution. To improve the accuracy of fingerprints, Guo Zhenhua suggested that the depth of learning applied to fingerprints. Deep learning is an end-to-end process that can be categorized and evaluated for different scenarios. He said that based on the traditional experience of the method, when the amount of data reaches a certain level, its performance may also have reached a bottleneck, while the depth of learning you can with the increase of the amount of data, performance gradually increased, thereby enhancing the accuracy of fingerprinting.
For fingerprint security issues, Guo Zhenhua said the need to use Optical coherence tomography technology, referred to as OCT, optical tomography, the technology originally used in medicine, such as cardiovascular disease diagnosis, eye disease diagnosis, in vivo testing, recently started Fingerprint recognition.
The technology uses near-infrared light and optical interference principle to slice the biological tissue. It not only can capture the inner and outer fingerprint, but also can detect the sweat gland between the two layers of fingerprint, but the fingerprint is lack of fingerprints Sweat glands and internal fingerprinting structures. Visible, the application of the technology is more conducive to security.
In addition, based on the OCT technique, Optical Anigiography (OAG), or optical microangiography, can also be used to process the signals collected by the OCT and effectively separate the static scattering particles and the dynamic scattering particles in the sample.
Guo Zhenhua also milk flow in the pipe as an example, introduced the difference between dynamic and static information, that is, the higher the flow rate, the higher the dynamic part of the intensity. And return to the fingerprint, it can be resolved without fingerprints of blood flow information, real life fingerprint detection.
Although fingerprint recognition is slightly dim under the fiery face recognition, as a matured recognition method that has been developed for many years, it has become the standard of smartphones. In the future, it will continue to popularize into the low-end market and its market scale is still Steady growth. And joined the deep learning and biometric detection of fingerprinting, while enhancing accuracy and enhance security features, but also increased the difficulty and cost of attack, fingerprint security enhancements are also largely contribute to its larger Application space, its face recognition and other biometric identification technology there is great uncertainty.

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