TY  - JOUR
AU  - Admin, System
AU  - Hafizur, Md
TI  - Robustness and Generalization of Lightweight Face-Verification Models: A Reproducible Cross-Dataset Evaluation Framework for Resource-Constrained Deployment
JO  - Journal of Engineering Applications and Intelligent Systems
PY  - 2026
VL  - 1
IS  - 1
SP  - 1
EP  - 19
AB  - Lightweight face-verification models suit deployments with limited compute, including CPU-only servers, but performance on one benchmark may not transfer to other poses, ages, resolutions, compression levels or capture conditions. This paper specifies a reproducible framework for measuring robustness and generalization of lightweight face-verification models across datasets and capture conditions, with within-dataset, fixed-threshold cross-dataset, target-recalibrated, controlled-degradation and CPU-efficiency tracks, identity-aware bootstrap intervals, and an open toolkit implementing every metric and degradation. In an executed study, three lightweight models (MobileFaceNet-w600k, SFace and a Qualcomm MobileFaceNet) and an ArcFace-R50 reference were evaluated on LFW, CFP-FP and AgeDB-30 and on a 22-condition degradation grid on LFW. The lightweight models had overlapping clean LFW EER (0.43%, 0.57% and 0.67%; reference 0.30%) but differed from 5.51% to 14.60% on CFP-FP and from 3.70% to 5.70% on AgeDB-30; the paired difference between the Qualcomm model and MobileFaceNet-w600k on CFP-FP was -9.1 points (A − C). A threshold calibrated on LFW at FMR = 1% gave FMR above 1% on AgeDB-30 for all four models (1.83%–3.23%); recalibration restored FMR at the cost of higher FNMR and needed about 1,000 target pairs. Under strong blur, 20-pixel resolution and a lower-face band the models diverged by several-fold, and under the largest band a frozen clean threshold gave FMR of 32.53%, 4.20%, 93.13% and 18.73% (A, B, C, reference). Single-thread CPU latency was 13.7, 24.9, 11.3 and 287.6 ms per image; speed did not predict robustness. CFP-FP and AgeDB-30 came from an unofficial redistribution with approximated identities, so those results are provisional pending replication on the official releases. The framework helps practitioners identify where a lightweight model remains reliable, where thresholds become unstable, and when recapture or human review is preferable to automatic decisions.
KW  - face verification; lightweight deep learning; robustness; generalization; cross-dataset evaluation; domain shift; threshold calibration; CPU inference
SN  - Not Available
DO  - 10.64886/jeais.0001
UR  - https://jeais.org/article/3
ER  -