With the massive growth of 3C products and personal data, user identification that controls the access right has gained a lot more interest. Among these methods, the use of biometrics for identification has gradually replaced traditional knowledge-based methods and become the mainstream. For example, fingerprint has already occupied a vast market in the smartphone authentication system. Novel in-display fingerprint technique can directly install sensor under display screen, so no additional space is required to place the sensor, which is more convenient to use and provides a solution to perform fingerprint authentication in full-screen smartphones. However, in-display fingerprint is easily affected by the noise of display panel and related circuits, which causes poor image quality. If we want to use traditional local-minutiae-based method, it is necessary to use high-computation and long-latency reconstruction methods to improve fingerprint quality. However, this is not friendly to edge devices that require low computational complexity. Based on the above problems, this thesis uses global orientation field to extract features by observing the angle distribution to create the frequency vector. Since the orientation field is more tolerant to noise, high-complexity reconstruction methods can be avoided. To further improve the recognition ability, we use triplet loss to train the neural network model and change the original frequency vector into a transformed vector, where it can gather self-features while pushing away other features. Finally, we use a matching-based method for fingerprint authentication. Meanwhile, a vote decision method is used to enlarge the score gap between self and other data to obtain a better result. This method doesn’t require high-complexity reconstruction processes; therefore, it is more suitable for edge devices.