  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.
