This paper introduces BlinkFusion, a real-time, interpretable, and detector-independent pipeline for analyzing blink events. The system finds eye ROIs using YOLOv5 as the main detector and a Haar cascade as a backup that has been calibrated. It then stabilizes the detections using lightweight tracking. A small landmark regressor inside each ROI gives six points to calculate the Eye Aspect Ratio (EAR), which keeps the geometric meaning. An uncertainty-weighted smoother combines pose, landmark, and detector confidence. An online hysteresis state machine with minimum-duration and derivative gates makes blink onsets and offsets. Platt-calibrated and fused detector confidences make strong arbitration possible at a reasonable cost. Performance is assessed on EyeBlinkDB (RGB ≥25 fps, ~720p), using subject-independent 10-fold splits and temporal-IoU matching. The model achieves Precision 0.928 ± 0.008, Recall 0.945 ± 0.008, F1 0.936 ± 0.008, AP@tIoU=0.3 = 0.962 ± 0.006, with timing precision of 28.1 ± 1.7 ms onset MAE and 37.1 ± 2.3 ms offset MAE. Condition stratification validates robustness: The values for Bright/Dim F1 are 0.947/0.922, for Frontal/±15°/≥30° yaw F1 they are 0.952/0.936/0.903, for Glasses (No/Yes) they are 0.946/0.925, and for Occlusion (No/Yes) they are 0.948/0.906. With adaptive scheduling (YOLO invocation rate ρ≈0.045), pipeline runs at about 32 FPS on the CPU (about 90 FPS on the observed frame loop) with a fixed 3-frame delay, which is fast enough for real-time use on cheap hardware. Ablations demonstrate progressive improvements resulting from calibrated fusion, tracking, quality-weighted EMA, and hysteresis (F1: 0.903 → 0.952). The approach is modular (you may switch the detector), doesn't need bounding boxes, and makes judgments that can be explained using EAR traces and thresholds. So, BlinkFusion is a useful, ready-to-use solution for HCI and clinical contexts that need clear, accurate, and quick blink analytics.

The Recurrent Neural Network (RNN) is well suited for emotional speech recognition because its uses constantly time shifting property. Even though RNN gives better results GRU, LSTM and BILSTM solves the gradient problem and overfitting problem joins the path to reduces the efficiency. Hence in this paper five deep learning architecture is designed in order to overcome the major issues using data augmentation and spatial feature. Five different architectures like: Enhanced Deep Hierarchal LSTM & GRU (EDHLG), EDHBG, EDHGL, EDHGB & EDHGG are developed with dropout layers. The raw data learned from LSTM will be given as the input to GRU layer for deepest learning. Thus, the gradient problem is reduced, and accuracy of each emotion was increased. Also, to enhance the accuracy level spatial features were concatenated with MFCC. Thus, in all models, the experimental evaluation with the Tamil emotional dataset yielded the best results. EDHLG has a 93.12% accuracy, EDHGL has a 92.56 percent accuracy, EDHBG has a 95.42 percent accuracy, EDHGB has a 96 percent accuracy, and EDHGG has a 94 percent accuracy. Furthermore, the average accuracy rate of a single individual LSTM layer is 74%, while BILSTM is 77%. EDHGB outperforms almost all other systems, by an optimal system of 94.27 percent and then a maximum overall accuracy of 95.99 percent. For the Tamil emotion data, emotional states such as happy, fearful, angry, sad, and neutral have a 100% prediction accuracy, while disgust has a 94 percent efficiency rate and boredom has an 82 percent accuracy rate. Also, the training time and evaluation time utilized by EDHGB is 4.43 mins and 0.42 mins which is less when compared with other models. Hence by changing the LSTM, BILSTM and GRU layers large analysis of experiment on Tamil dataset is done and EDHGB is superior to other models, and when compared with basic models LSTM and BILSTM around 26% more efficiency is gained.