Personalized two-stage comparison-based framework for low-to-mid-intensity facial expression recognition in real-world scenarios

• Two-Stage comparison improves near-neutral facial expression recognition. • Personalized baselines adapt to individual expression dynamics. • Pairwise/ordinal ranking enhances low-to-mid-intensity discrimination. • Cross-age evaluation shows robust performance on young and elderly individuals. • Interpretable analysis clarify why the method outperforms common facial expression recognition toolkits. We evaluate a personalized, two-stage comparison-based FER framework on two datasets of low-to-mid-intensity, near-neutral expressions. The framework consistently outperforms FaceReader and Py-Feat. On the natural-transition younger-adult dataset (Dataset A, n = 9), mean accuracy is 90.22% ± 3.53%, with within-subject median gains of +16.46 percentage points (pp) over FaceReader (95% CI [+11.33, +33.90], p = 0.00195, r = 1.00) and +8.17 pp over Py-Feat (95% CI [+3.39, +21.58], p = 0.00195, r = 1.00). On the older adults dataset (Dataset B, n = 78), mean accuracy is 75.58% ± 9.04%, exceeding FaceReader by +15.47 pp (95% CI [+13.44, +17.21], p = 2.77 × 10 -14 , r = 0.980) and Py-Feat by +17.67 pp (95% CI [+15.13, +19.34], p = 3.02 × 10 -14 , r = 0.985). Component analyses are above chance on both datasets (B-stage medians 92.90% and 99.51%), and polarity-specific asymmetries emerge in the C-stage (A: positive > negative, Δ = +4.23 pp, two-sided p = 0.0273; B: negative > positive, Δ = -7.72 pp, p = 0.00442). On a subset of Dataset A emphasizing subtle transitions, the system maintains [78.61%, 85.38%] accuracy where human annotation accuracy ranges [50.00%, 71.47%]. Grad-CAM highlights eyebrows, forehead, and mouth regions consistent with expressive cues. Collectively, these findings demonstrate statistically significant and practically meaningful advantages for low-to-mid-intensity expression recognition and intensity ranking.