Abstract: An image authentication method and a real-time product authentication system are provided. The method includes the following steps: inputting a training image in a training phase; segmenting the training image into a plurality of training sub-images; and training the deep learning model by using the plurality of training sub-images, wherein each of the plurality of training images includes a global feature property. The steps in the testing phase include: inputting a testing image and segmenting the testing image into a plurality of testing sub-images; performing a test on each of the plurality of testing sub-images and checking whether each of the plurality of testing sub-images is associated with one of the plurality of categories; and outputting an authentication result to identify whether the testing image is associated with one of the plurality of categories.

Abstract: An intelligent gait analyzing apparatus comprises a first camera and an electronic device. The first camera captures a first video in which the patient walks and a second video in which the patient changes a posture from standing up to sitting down. A second camera may be used to capture a three-dimensional point cloud diagram of the patient to generate a chest skeleton and an abdomen skeleton. A 3D skeleton model and a moving diagram may be created based on the first video. The memory stores a long short-term memory model, a transformer machine learning model and an algorithm of modified gravity center. The graphic processor calculates a gravity center deviation based on the moving diagram, the chest skeleton and the abdomen skeleton. The graphic processor acquires a stride, a pace, a humpbacked value and a risk of falling.

Abstract: An image authentication method and a real-time product authentication system are provided. The method includes the following steps: inputting a training image in a training phase; segmenting the training image into a plurality of training sub-images; and training the deep learning model by using the plurality of training sub-images, wherein each of the plurality of training images includes a global feature property. The steps in the testing phase include: inputting a testing image and segmenting the testing image into a plurality of testing sub-images; performing a test on each of the plurality of testing sub-images and checking whether each of the plurality of testing sub-images is associated with one of the plurality of categories; and outputting an authentication result to identify whether the testing image is associated with one of the plurality of categories.