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Novel Deep Learning Methods for Identifying Human Emotions: A Comprehensive Review
Human emotion recognition has emerged as a critical component in the development of intelligent systems capable of understanding and responding to human emotional states. This comprehensive review examines the latest novel deep learning methods for emotion recognition, encompassing advances in neural architectures, multimodal fusion techniques, and innovative approaches to feature extraction and classification. The field has witnessed remarkable progress through the integration of sophisticated deep learning models including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), transformer architectures, and generative adversarial networks (GANs). Recent breakthroughs have demonstrated exceptional performance improvements, with some methods achieving over 99% accuracy on benchmark datasets such as DEAP, SEED, and MAHNOB-HCI. This review analyzes the evolution from traditional machine learning approaches to state-of-the-art deep learning methodologies, examining their effectiveness across various modalities including electroencephalography (EEG), facial expressions, speech, and physiological signals. The paper provides a systematic analysis of fusion strategies, architectural innovations, and performance metrics while identifying key challenges and future research directions in the rapidly evolving landscape of emotion recognition technology.
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