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@article{197371,
        author = {Dr. Mohd. Arif and Shivam Gupta and Jitesh Dagar and Gurdeep Chaudhary},
        title = {Neural Echoes: Temporal Modeling of Facial Expressions Using Deep Recurrent Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6840-6844},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197371},
        abstract = {Facial expression recognition (FER) remains a challenging problem in computer vision due to the temporal and dynamic nature of human emotions. This paper presents a novel deep learning framework leveraging Recurrent Neural Networks (RNNs) to capture sequential dependencies in facial expressions across video sequences. We propose a hybrid architecture combining Convolutional Neural Networks (CNNs) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal modeling. Our approach achieves state-of-the-art performance: 96.2% accuracy on CK+, 72.4% on FER2013, and 85.7% on RAF-DB. Temporal modeling improves recognition accuracy by 4–8 percentage points over static frame-based methods. Comprehensive ablation studies examine network depth, sequence length, bidirectionality, and variants of the Gated Recurrent Unit (GRU). The system maintains real-time computational efficiency at 45 fps on the GPU.},
        keywords = {Facial Expression Recognition (FER), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Deep Learning, Affective Computing, Spatiotemporal Modeling, Human-Computer Interaction (HCI), Transfer Learning, Bidirectional RNN, Temporal Attention.},
        month = {April},
        }