Facial Expression Recognition: A Review of Advanced Machine Learning Techniques
Abstract
Facial Expression Recognition (FER) has become essential in diverse domains such as human-computer interaction, healthcare, security, and social robotics. This review paper examines the evolution of FER, highlighting the transition from conventional machine learning algorithms to advanced deep learning techniques. Initially, FER systems heavily depended on handcrafted features and conventional classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), using techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP). Although foundational, these methods struggled with challenges like lighting, pose, and occlusions. The emergence of deep learning has transformed FER, with Convolutional Neural Networks (CNNs) that automatically learn hierarchical feature representations from raw image data, greatly improving recognition accuracy. Moreover, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have been utilized to capture the temporal dynamics of facial expressions. This review also emphasizes the latest deep learning algorithms, including CNNs and LSTMs, as well as the growing role of transformer models with attention mechanisms. Despite these advancements, FER remains challenging due to intra-class variability and contextual influences. Future research directions involve developing more robust algorithms, integrating multimodal data, and addressing ethical concerns such as privacy and bias mitigation. This paper aims to offer a brief overview of FER’s current state, guiding future research and applications in this dynamic field.
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Li, Shan, and Weihong Deng. "Deep facial expression recognition: A survey." IEEE transactions on affective computing 13.3 (2020): 1195-1215.
Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Vol. 1. Ieee, 2005.
Ojala, Timo, Matti Pietikäinen, and David Harwood. "A comparative study of texture measures with classification based on featured distributions." Pattern recognition 29.1 (1996): 51-59.
Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Donahue, Jeffrey, et al. "Long-term recurrent convolutional networks for visual recognition and description." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems 27 (2014).
Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).
Umar, Abubakar, at al. “Improved Face Recognition Based on Discrete Bat Algorithm for Feature Selection Scheme.
Sariyanidi, Evangelos, Hatice Gunes, and Andrea Cavallaro. "Automatic analysis of facial affect: A survey of registration, representation, and recognition." IEEE transactions on pattern analysis and machine intelligence 37.6 (2014): 1113-1133.
Abdulrahman, Muzammil, et al. "Gabor wavelet transform based facial expression recognition using PCA and LBP." 2014 22nd signal processing and communications applications conference (SIU). IEEE, 2014.
Abdulrahman, Muzammil, and Alaa Eleyan. "Facial expression recognition using support vector machines." 2015 23nd signal processing and communications applications conference (SIU). IEEE, 2015.
Niu, Ben, Zhenxing Gao, and Bingbing Guo. "Facial expression recognition with LBP and ORB features." Computational Intelligence and Neuroscience 2021.1 (2021): 8828245.
Ravi, Rahul, and S. V. Yadhukrishna. "A face expression recognition using CNN & LBP." 2020 fourth international conference on computing methodologies and communication (ICCMC). IEEE, 2020.
Jayanthi, P., et al. "An Enhanced Method for Recognition of Facial Expressions using Convolutional Neural Network." 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2024.
Li, Jing, et al. "Attention mechanism-based CNN for facial expression recognition." Neurocomputing 411 (2020): 340-350.
Chouhayebi, Hajar, et al. "Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM." Computers 13.4 (2024): 101.
Ma, Fuyan, Bin Sun, and Shutao Li. "Facial expression recognition with visual transformers and attentional selective fusion." IEEE Transactions on Affective Computing 14.2 (2021): 1236-1248.
Hebri, Dheeraj, et al. "Effective facial expression recognition system using machine learning." EAI Endorsed Transactions on Internet of Things 10 (2024).
Akhand, M. A. H., et al. "Facial emotion recognition using transfer learning in the deep CNN." Electronics 10.9 (2021): 1036.
Kim, Sangwon, Jaeyeal Nam, and Byoung Chul Ko. "Facial expression recognition based on squeeze vision transformer." Sensors 22.10 (2022): 3729.
Liao, Jun, et al. "Facial expression recognition methods in the wild based on fusion feature of attention mechanism and LBP." Sensors 23.9 (2023): 4204.
Li, Bin, and Dimas Lima. "Facial expression recognition via ResNet-50." International Journal of Cognitive Computing in Engineering 2 (2021): 57-64.
Pranav, E., et al. "Facial emotion recognition using deep convolutional neural network." 2020 6th International conference on advanced computing and communication Systems (ICACCS). IEEE, 2020.
Minaee, Shervin, Mehdi Minaei, and Amirali Abdolrashidi. "Deep-emotion: Facial expression recognition using attentional convolutional network." Sensors 21.9 (2021): 3046.
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