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Revisiting Facial Key Point Detection - an Efficient Approach Using Deep Neural Networks

EasyChair Preprint no. 7903

15 pagesDate: May 4, 2022


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 characteristics 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.

Keyphrases: CNN, Convolutional Neural Network, Custom CNN, deep learning, Efficient Transfer Learning, facial key point detection, Facial keypoint detection, Facial Landmark Detection, inference time, Keras-autotuner, MobileNetV2, NASNetMobile

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Prathima Dileep and Bharath Bolla and Sabeesh Ethiraj},
  title = {Revisiting Facial Key Point Detection - an Efficient Approach Using Deep Neural Networks},
  howpublished = {EasyChair Preprint no. 7903},

  year = {EasyChair, 2022}}
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