TY - GEN
T1 - Multifeature Fusion for Facial Expression Recognition
AU - Wunake, Patrick
AU - Boante, Leonard Mensah
AU - Wilson, Matilda Serwaa
AU - Appati, Justice Kwame
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This paper introduces a novel approach for FER utilizing multifeature fusion based on supervised learning, leveraging the inherent strengths of LBP, HOG, and SIFT descriptors, a combinatorial technique was employed to discern the most efficient fusion methodology. Two fusion strategies were assessed: direct concatenation and the Z-score method, with the latter demonstrating superior computational efficacy. Normalization was introduced, to enhance descriptor performance. Subject-dependent (SD) and subject-independent (SI) were employed; comparative evaluations showcased the accuracy of our approach’s (100%) proficiency on JAFFE, CK+, and FER2013 datasets compared to recent works (99.2%) (Comput Intell Neurosci 2021:1–10). Despite the notable outcomes, the system’s dependency on well-labeled datasets was a limitation. Consequently, future research avenues are suggested to incorporate poorly labeled and unlabeled datasets, facilitating the exploration of less structured datasets. This study paves the way for advancing FER, underscoring the potential of multifeature fusion and the importance of rigorous feature normalization.
AB - This paper introduces a novel approach for FER utilizing multifeature fusion based on supervised learning, leveraging the inherent strengths of LBP, HOG, and SIFT descriptors, a combinatorial technique was employed to discern the most efficient fusion methodology. Two fusion strategies were assessed: direct concatenation and the Z-score method, with the latter demonstrating superior computational efficacy. Normalization was introduced, to enhance descriptor performance. Subject-dependent (SD) and subject-independent (SI) were employed; comparative evaluations showcased the accuracy of our approach’s (100%) proficiency on JAFFE, CK+, and FER2013 datasets compared to recent works (99.2%) (Comput Intell Neurosci 2021:1–10). Despite the notable outcomes, the system’s dependency on well-labeled datasets was a limitation. Consequently, future research avenues are suggested to incorporate poorly labeled and unlabeled datasets, facilitating the exploration of less structured datasets. This study paves the way for advancing FER, underscoring the potential of multifeature fusion and the importance of rigorous feature normalization.
KW - Descriptors
KW - Facial expression
KW - Fusion
KW - Machine learning
KW - Normalization
UR - http://www.scopus.com/inward/record.url?scp=85199419554&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2053-8_12
DO - 10.1007/978-981-97-2053-8_12
M3 - Conference contribution
AN - SCOPUS:85199419554
SN - 9789819720521
T3 - Lecture Notes in Networks and Systems
SP - 157
EP - 168
BT - Communication and Intelligent Systems - Proceedings of ICCIS 2023
A2 - Sharma, Harish
A2 - Shrivastava, Vivek
A2 - Tripathi, Ashish Kumar
A2 - Wang, Lipo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Communication and Intelligent Systems, ICCIS 2023
Y2 - 16 December 2023 through 17 December 2023
ER -