TY - GEN
T1 - Convolution Neural Network for Pose Estimation of Noisy Three-Dimensional Face Images
AU - Kamanditya, Bharindra
AU - Kuswara, Randy Pangestu
AU - Nugroho, Muhammad Adi
AU - Putro, Benyamin Kusumo
PY - 2019/1/28
Y1 - 2019/1/28
N2 - From limited two-dimensional recognition, facial recognition has now been developed to be able to recognize three-dimensional face images, which usually involves process of face pose estimation. As the conventional artificial neural networks has shown low recognition rate to this problem, Convolution Neural Network have been the most potential classifier to determine the pose estimation of a three-dimensional face images. Convolution operation is expected to minimize the effect of distortion and disorientation of the object, and able to efficiently reduce the required parameters. Results show that the CNN system could estimate the pose position of the 3D face images with high recognition rate, however, this recognition rate decline significantly for the noisy buried face images, showing the CNN still need improvement to deal with noisy environments.
AB - From limited two-dimensional recognition, facial recognition has now been developed to be able to recognize three-dimensional face images, which usually involves process of face pose estimation. As the conventional artificial neural networks has shown low recognition rate to this problem, Convolution Neural Network have been the most potential classifier to determine the pose estimation of a three-dimensional face images. Convolution operation is expected to minimize the effect of distortion and disorientation of the object, and able to efficiently reduce the required parameters. Results show that the CNN system could estimate the pose position of the 3D face images with high recognition rate, however, this recognition rate decline significantly for the noisy buried face images, showing the CNN still need improvement to deal with noisy environments.
KW - convolutional neural network
KW - face recognition
KW - head pose estimation
KW - hyperparameter evaluation
KW - image noises
UR - http://www.scopus.com/inward/record.url?scp=85062888878&partnerID=8YFLogxK
U2 - 10.1109/ICETAS.2018.8629150
DO - 10.1109/ICETAS.2018.8629150
M3 - Conference contribution
AN - SCOPUS:85062888878
T3 - 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences, ICETAS 2018
BT - 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences, ICETAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2018
Y2 - 22 November 2018 through 23 November 2018
ER -