Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
This addresses the problem of accurately predicting facial key points coordinates for applications in computer vision and machine learning, but appears incremental as it adapts an existing architecture.
The paper tackled facial key points detection by proposing NaimishNet, a LeNet-adapted deep CNN model, and compared its performance against existing state-of-the-art approaches.
Facial Key Points (FKPs) Detection is an important and challenging problem in the fields of computer vision and machine learning. It involves predicting the co-ordinates of the FKPs, e.g. nose tip, center of eyes, etc, for a given face. In this paper, we propose a LeNet adapted Deep CNN model - NaimishNet, to operate on facial key points data and compare our model's performance against existing state of the art approaches.