论文标题
重新访问面部要点检测：使用深神经网络的有效方法
Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks
论文作者
论文摘要
面部地标检测是一个广泛研究的深度学习领域，因为这在许多领域都有广泛的应用。这些要点是区分脸上的特征点，例如眼睛中心，眼睛的内角和外角，嘴中心以及可以解释人类情感和意图的鼻尖。我们工作的重点是评估转移学习模型，例如Mobilenetv2和Nasnetmobile，包括自定义CNN体系结构。这项研究的目的是根据模型大小，参数和推理时间开发有效的深度学习模型，并研究增强插补和微调对这些模型的影响。据发现，尽管增强技术的RMSE得分比归纳技术较低，但它们并没有影响推理时间。 Mobilenetv2体系结构产生了最低的RMSE和推理时间。此外，我们的结果表明，手动优化的CNN体系结构与自动Keras Tuned Architecture相似。但是，手动优化的体系结构产生了更好的推理时间和训练曲线。
Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eyes center, the eye's inner and outer corners, the mouth center, and the nose tip from which human emotions and intent can be explained. The focus of our work has been evaluating transfer learning models such as MobileNetV2 and NasNetMobile, including custom CNN architectures. The objective of the research has been to develop efficient deep learning models in terms of model size, parameters, and inference time and to study the effect of augmentation imputation and fine-tuning on these models. It was found that while augmentation techniques produced lower RMSE scores than imputation techniques, they did not affect the inference time. MobileNetV2 architecture produced the lowest RMSE and inference time. Moreover, our results indicate that manually optimized CNN architectures performed similarly to Auto Keras tuned architecture. However, manually optimized architectures yielded better inference time and training curves.
