TY - GEN
T1 - Automatic Physiognomy System using Active Appearance Model and Convolutional Neural Network
AU - Sudiana, Dodi
AU - Rizkinia, Mia
AU - Rafid, Ilham Mulya
N1 - Funding Information:
This research is funded by Publikasi Terindeks Internasional (PUTI) Q2 Research Grant based on contract No. NKB-4326/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
©2021 IEEE
PY - 2021
Y1 - 2021
N2 - This research discusses the design and development of an automatic physiognomy system to determine a person’s tendencies based on the features of its face. Physiognomy itself is a method of predicting a person’s characteristics based on their facial features. Each facial feature has its uniqueness and characteristics, such as variations in distance, overall shape, and size. The facial image as input data is processed in every system step. Finally, the system displays the personality of that person. Simulations show that each algorithm can perform its respective functions well. The simulation results show that the combination of extracting facial features using the Active Appearance Model and Convolutional Neural Network for solving classification problems produces a very good number of personality traits predictions with each model accuracy value between 0.8 to 1, or 0.8797 on average. In addition, the model made proved to produce a good performance for the classification process with a true positive rate between 0.8834 to 1, or 0.9417 on average. This method can also detect many personality traits, with 28 personality traits that can be detected.
AB - This research discusses the design and development of an automatic physiognomy system to determine a person’s tendencies based on the features of its face. Physiognomy itself is a method of predicting a person’s characteristics based on their facial features. Each facial feature has its uniqueness and characteristics, such as variations in distance, overall shape, and size. The facial image as input data is processed in every system step. Finally, the system displays the personality of that person. Simulations show that each algorithm can perform its respective functions well. The simulation results show that the combination of extracting facial features using the Active Appearance Model and Convolutional Neural Network for solving classification problems produces a very good number of personality traits predictions with each model accuracy value between 0.8 to 1, or 0.8797 on average. In addition, the model made proved to produce a good performance for the classification process with a true positive rate between 0.8834 to 1, or 0.9417 on average. This method can also detect many personality traits, with 28 personality traits that can be detected.
KW - Active Appearance Model (AAM)
KW - Convolutional Neural Network (CNN)
KW - image processing
KW - personality detection
KW - physiognomy
UR - http://www.scopus.com/inward/record.url?scp=85126919212&partnerID=8YFLogxK
U2 - 10.1109/QIR54354.2021.9716162
DO - 10.1109/QIR54354.2021.9716162
M3 - Conference contribution
AN - SCOPUS:85126919212
T3 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
SP - 104
EP - 109
BT - 17th International Conference on Quality in Research, QIR 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
Y2 - 13 October 2021 through 15 October 2021
ER -