TY - GEN
T1 - A Novel Approach for Detecting Facial Key Points Using Convolution Neural Networks
AU - Kakkar, Rishi
AU - Murthy, Y. V.Srinivasa
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The task of face recognition is having many real-time applications in which the process of facial keypoint detection is considered to be an intermediate and crucial step. The amount of keypoints that are using for face recognition decides the computational requirements of the algorithm. In this paper, an effort has been made to detect the useful 15 facial key points using convolutional neural networks and compared with the state-of-the-art system with 30 facial key points. We made an effort to identify the 15 facial key points (6 points from eye +4 points from eyebrows +4 points from lips +1 point from the nose) by using the proper hyperparameters for convolutional neural network. It is found that the performance of the proposed system is quite similar when compared to the system with 30 facial key points.
AB - The task of face recognition is having many real-time applications in which the process of facial keypoint detection is considered to be an intermediate and crucial step. The amount of keypoints that are using for face recognition decides the computational requirements of the algorithm. In this paper, an effort has been made to detect the useful 15 facial key points using convolutional neural networks and compared with the state-of-the-art system with 30 facial key points. We made an effort to identify the 15 facial key points (6 points from eye +4 points from eyebrows +4 points from lips +1 point from the nose) by using the proper hyperparameters for convolutional neural network. It is found that the performance of the proposed system is quite similar when compared to the system with 30 facial key points.
UR - https://www.scopus.com/pages/publications/85124671935
UR - https://www.scopus.com/pages/publications/85124671935#tab=citedBy
U2 - 10.1007/978-3-030-95711-7_50
DO - 10.1007/978-3-030-95711-7_50
M3 - Conference contribution
AN - SCOPUS:85124671935
SN - 9783030957100
T3 - Communications in Computer and Information Science
SP - 616
EP - 625
BT - Artificial Intelligence and Speech Technology - 3rd International Conference, AIST 2021, Revised Selected Papers
A2 - Dev, Amita
A2 - Agrawal, S. S.
A2 - Sharma, Arun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Artificial Intelligence and Speech Technology, AIST 2021
Y2 - 12 November 2021 through 13 November 2021
ER -