TY - GEN
T1 - Analysis of CNN Architectures for Pose Estimation of Noisy 3-D Face Images
AU - Kuswana, Randy Pangestu
AU - Akhmad, Faqih
AU - Kusumoputro, Benyamin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Convolutional neural networks (CNN) has been used in various applications, especially in computer vision field, due to its superiority compare with that of conventional artificial neural networks. In this paper, CNN is developed as head pose estimator for noisy three dimensional face images and analyzed the recognition accuracy for different architectural architecture of the networks, especially on the feature extraction part. Four different amount of layers are experimented, which resulting different input neurons to the estimator part. Experimental results show that the CNN could estimate the head pose with high enough recognition accuracy, and the CNN with 3 feature extraction layers could obtain the highest accuracy of 81.31% for normal face images.
AB - Convolutional neural networks (CNN) has been used in various applications, especially in computer vision field, due to its superiority compare with that of conventional artificial neural networks. In this paper, CNN is developed as head pose estimator for noisy three dimensional face images and analyzed the recognition accuracy for different architectural architecture of the networks, especially on the feature extraction part. Four different amount of layers are experimented, which resulting different input neurons to the estimator part. Experimental results show that the CNN could estimate the head pose with high enough recognition accuracy, and the CNN with 3 feature extraction layers could obtain the highest accuracy of 81.31% for normal face images.
KW - convolutional neural network
KW - deep learning
KW - head pose estimation
KW - image processing
UR - http://www.scopus.com/inward/record.url?scp=85085562480&partnerID=8YFLogxK
U2 - 10.1109/ICSPIS48135.2019.9045906
DO - 10.1109/ICSPIS48135.2019.9045906
M3 - Conference contribution
AN - SCOPUS:85085562480
T3 - 2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019
BT - 2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019
Y2 - 30 October 2019 through 31 October 2019
ER -