TY  - MANSCPT
AU  - Anbananthan Pillai A/l Munanday,
TI  - Transfer learning-based deep  learning models for intelligent  human frontal facial emotion  recognition
SN  - THE0009971 (Local)
PY  - 2024///
CY  - Kuantan, Pahang
PB  - UMPSA
KW  - Faculty of Manufacturing and Mechatronic Engineering Technology
KW  - Dissertations
KW  - Universities and colleges
KW  - Theses
N1  - Faculty of Manufacturing and Mechatronic Engineering Technology; Thesis (Master of Science) -- Universiti Malaysia Pahang – 2024; Includes bibliographical references
N2  - Achieving precise facial emotion recognition (FER) is a critical endeavor in artificial intelligence to enhance interactions between humans and computers. However, current Convolutional Neural  Network (CNN) architectures face substantial obstacles, including structural limitations and  reliance on controlled datasets, leading to diminished accuracy and processing speed. These  issues hinder the ability to accurately capture and interpret nuanced human emotions, especially  in non-frontal or profile images. This study addresses the recognizing and interpreting human  emotions from facial expressions by using an improved Convolutional Neural Network (CNN)  architecture. This architecture is an adaptation of the well-known VGG-16 model. Numerous  research efforts have applied to CNNs, incorporating a restricted layer structure, to solve  challenges in Facial Emotion Recognition (FER). Nonetheless, conventional shallow CNNs, with  their basic learning strategies, struggle to adequately extract features necessary for discerning  emotional content in high-definition images. A common limitation among many existing  approaches is their focus solely on frontal images, disregarding the significance of profile images  captured from various angles, which are crucial for the functionality of a comprehensive FER  system. To forge a highly precise FER system, this research introduces an advanced Deep CNN  (DCNN) approach via the Transfer Learning (TL) method. This method involves the use of a preexisting DCNN framework, specifically VGG-16, by altering its upper dense layer(s) to suit FER  requirements, and further refining the model with facial emotion datasets. The efficacy of this  refined CNN model is evaluated against five different pre-trained DCNN architectures (ResNet50, VGG-16, MobileNetV2, EfficientNetB0, Xception) using three benchmark datasets  FER2013, CK+, own dataset and KDEF with 80:20 training and testing split. The study focused  on optimizing different parameters of the CNN, including the number of layers, activation  functions, learning rates, and dropout rates. These adjustments were aimed at enhancing the  performance of facial emotion recognition (FER) across several datasets. The goal was to  accurately identify the seven universally recognized facial expressions: happiness, sadness,  anger, fear, disgust, surprise, and neutrality with both frontal and side views. The suggested  approach attained significant precision on these datasets using pre-trained models. Through a 10- fold cross-validation method, the highest facial emotion recognition (FER) accuracies obtained  with a refined CNN model on the test sets of FER-2013, CK+, own dataset and KDEF were  93.02%, 97.62%, 93.95% and 86.04%, respectively. Additionally, the performance on the KDEF  dataset, specifically with profile views, was encouraging, showcasing the necessary effectiveness  for practical real-world application. The major findings indicated that the optimized CNN model  achieved significant improvements in test accuracy, reaching 95.47% on FER2013, 93.51% on  CK+, 91.11% on own dataset and 93.64% on KDEF, outperforming the traditional VGG-16  model and several other benchmarks from the literature. Significantly, the refined model  demonstrated the ability to identify emotions across a broad range of facial expressions,  encompassing profile views, among different demographic categories, and even when facial  features were partially obscured. This research not only highlights the improvements in accuracy  and generalizability of the refined CNN model but also sets the stage for future studies. Future research will focus on integrating more diverse and complex datasets, exploring real-time FER  applications, and further optimizing CNN architectures to enhance their robustness and  applicability in real-world scenarios
ER  -