请翻译：Domain Adaptation适合在多种图像发生变化的情况例如设备或者采集目标或者环境发生改变时使用新设备采集少量实际用户的伪造和真实虹膜图像对已有模型进行快速的学习优化实现敏捷、较低成本的部署。具体的如虹膜图像采集设备更换或者调整的近红外补光光源时传统的活体检测算法等分类算法一般会完全失效需要重新采集大量的图像重新进行分类器的训练实用Domain Adaptation则可以采集少量新

Domain Adaptation is suitable for situations where there are multiple variations in images, such as changes in devices, collection targets, or environments. By using a small amount of fake and real iris images collected from actual users with new devices, the existing model can be quickly optimized for agile and low-cost deployment. Specifically, when the iris image acquisition device is replaced or the near-infrared supplementary light source is adjusted, traditional liveness detection algorithms and other classification algorithms generally become completely ineffective, requiring the collection of a large number of images for classifier retraining. Practical Domain Adaptation can optimize training with a small amount of new images to obtain a classification model suitable for the adjusted device. The paper conducts experiments and research on Domain Adaptation using liveness detection as an example, but the same method can also be used for other classification, detection, and recognition algorithm transfers