TY - GEN
T1 - Understanding Human Face Shape via Inception-ResNet Neural Network Architecture
AU - Hidayati, Shintami Chusnul
AU - Tasyanita, Jessica
AU - Fatichah, Chastine
AU - Anistyasari, Yeni
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In recent years, computer vision has advanced facial recognition applications across diverse domains, from security systems to augmented reality. Among the fundamental attributes of a face, shape plays a crucial role in distinguishing individuals and understanding their unique identities. This paper introduces an innovative approach to comprehending human face shape through the cutting-edge Inception-ResNet neural network architecture, which combines Inception with residual connections. The approach harnesses the rich discriminative features learned by this architecture to boost face shape recognition accuracy and robustness. Extensive experiments demonstrate the remarkable performance of the approach, surpassing traditional methods and prior deep learning models. Furthermore, to provide a holistic perspective on the individual contributions of various modules within our approach, we present a detailed ablation study.
AB - In recent years, computer vision has advanced facial recognition applications across diverse domains, from security systems to augmented reality. Among the fundamental attributes of a face, shape plays a crucial role in distinguishing individuals and understanding their unique identities. This paper introduces an innovative approach to comprehending human face shape through the cutting-edge Inception-ResNet neural network architecture, which combines Inception with residual connections. The approach harnesses the rich discriminative features learned by this architecture to boost face shape recognition accuracy and robustness. Extensive experiments demonstrate the remarkable performance of the approach, surpassing traditional methods and prior deep learning models. Furthermore, to provide a holistic perspective on the individual contributions of various modules within our approach, we present a detailed ablation study.
KW - Inception-Resnet architecture
KW - classification
KW - face shape
KW - facial recognition
KW - innovation
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85186519234&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427897
DO - 10.1109/ICAMIMIA60881.2023.10427897
M3 - Conference contribution
AN - SCOPUS:85186519234
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 631
EP - 636
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -