Robustness and Generalization of Lightweight Face-Verification Models: A Reproducible Cross-Dataset Evaluation Framework for Resource-Constrained Deployment

System Admin, Md Hafizur

Journal of Engineering Applications and Intelligent Systems · doi:10.64886/jeais.0001

Introduction

Face verification compares two facial samples and estimates whether they belong to the same person. Deep embedding models make this practical at scale, and lightweight architectures reduce latency and resource consumption. Yet computational efficiency does not guarantee robustness: a model can perform well on a familiar benchmark and degrade when the capture device, image-processing pipeline, pose, illumination, resolution, or compression changes.
A deployment threshold also affects generalization. A threshold calibrated on one dataset may not achieve the same FMR or FNMR on another. When a system supports admission, account access, or another consequential workflow, a shift in error rates creates both security risk (false accepts) and usability cost (false rejects). Robustness evaluation must therefore examine not only average verification accuracy but also operating-point stability, condition-specific errors, failure-to-process rates, and computational cost.
This paper proposes a protocol for assessing lightweight face-verification models across datasets and capture conditions. It emphasizes controlled comparisons, strict separation of calibration and test data, transparent reporting, and avoidance of unsupported claims. The protocol is released together with executable evaluation code so that any group can populate the result tables with identical procedures.