| dc.description.abstract | Face detection has always been a very popular topic, and in recent years, the rise of machine learning technology has pushed face detection to a higher level.
Training through a large amount of data can effectively improve the detection accuracy, so how to collect a larger number of face detection training pictures is an important topic.
In response to the research and development plan, the case of A Company in this study adopted a supervised learning method in the face detection project to train the face pictures to generate face detection modules, so there is often a demand for a large number of face pictures. Under long-term demand, the use of data generation and data augmentation is a very suitable way.
This study focuses on face data generation and face data augmentation, and explores which of the two methods is more efficient in producing face images, including the time it takes to generate the images and the availability of the generated images.
Comparing the two, we can find that the two are similar in terms of usability, but in terms of time efficiency, the way of using face data augmentation is much faster than the way of using face generation.
After confirming the efficiency and availability, it will be of great help to the future development of A company in this research case. | en_US |