TY - GEN
T1 - Spontaneous Macro and Micro Facial Expression Recognition Using ResNet50 and VLDSP
AU - Mendez, John
AU - Uddin, Md Azher
AU - Joolee, Joolekha Bibi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024/3/18
Y1 - 2024/3/18
N2 - Facial expressions serve as the predominant means of conveying human emotions, and their understanding has garnered significant interest among researchers due to the wide array of real-world applications it encompasses. However, there is limited research conducted on the simultaneous recognition of spontaneous macro expressions and micro expressions. This study introduces an innovative end-to-end framework designed to effectively process videos for spontaneous macro and micro facial expression recognition. The framework integrates the extraction of deep spatial features through ResNet50, and handcrafted spatiotemporal features obtained from the application of Volume Local Directional Structural Pattern (VLDSP). Additionally, a novel 1D Convolutional Neural Network (CNN) is introduced to classify facial expressions, utilizing the extracted deep spatial features and spatiotemporal features as input. Finally, a comprehensive experiment is conducted to demonstrate the proposed framework performance.
AB - Facial expressions serve as the predominant means of conveying human emotions, and their understanding has garnered significant interest among researchers due to the wide array of real-world applications it encompasses. However, there is limited research conducted on the simultaneous recognition of spontaneous macro expressions and micro expressions. This study introduces an innovative end-to-end framework designed to effectively process videos for spontaneous macro and micro facial expression recognition. The framework integrates the extraction of deep spatial features through ResNet50, and handcrafted spatiotemporal features obtained from the application of Volume Local Directional Structural Pattern (VLDSP). Additionally, a novel 1D Convolutional Neural Network (CNN) is introduced to classify facial expressions, utilizing the extracted deep spatial features and spatiotemporal features as input. Finally, a comprehensive experiment is conducted to demonstrate the proposed framework performance.
KW - 1D convolutional neural network
KW - ResNet50
KW - Spontaneous macro and micro facial expression recognition
KW - Volume local directional structural pattern
UR - http://www.scopus.com/inward/record.url?scp=85189565594&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8324-7_15
DO - 10.1007/978-981-99-8324-7_15
M3 - Conference contribution
AN - SCOPUS:85189565594
SN - 9789819983230
T3 - Lecture Notes in Networks and Systems
SP - 159
EP - 170
BT - Proceedings of International Conference on Information Technology and Applications
A2 - Ullah, Abrar
A2 - Anwar, Sajid
A2 - Calandra, Davide
A2 - Di Fuccio, Raffaele
PB - Springer
T2 - 16th International Conference on Information Technology and Applications 2022
Y2 - 20 October 2022 through 22 October 2022
ER -