Constraint-Aware Evaluation of Lightweight Classifiers for Biometric Authentication

Biometric authentication is increasingly deployed on devices with limited enrollment data, computation, and storage, yet classifier comparisons are still commonly reported as single-point accuracies obtained on data-rich benchmarks. This paper presents a constraint-aware empirical evaluation of five lightweight classifier families for biometric verification—k-nearest neighbors (k-NN), linear discriminant analysis (LDA), a standardized deviation threshold rule, a linear support vector machine (SVM), and a shallow multilayer perceptron (MLP)—rather than proposing a new classifier. On the CMU keystroke dynamics benchmark, a session-disjoint protocol (enrollment on sessions 1–6, verification on sessions 7–8) varies the enrollment size (N = 10, 50, 200) against a fixed held-out test set, controls the genuine-to-impostor ratio, selects every hyperparameter on training-only validation splits, and reports the receiver operating characteristic (ROC)-interpolated equal error rate (EER) with subject-level 95% confidence intervals across 51 subjects. Linear SVM achieves the lowest mean EER at every enrollment size (10.5%, 8.6%, and 8.0%). The gaps among SVM, LDA, and k-NN narrow markedly as enrollment grows; at N = 200, k-NN differs significantly from neither LDA nor SVM, and the LDA–SVM difference, though statistically significant, is only 0.41 percentage points. The evaluated shallow MLP is especially sensitive to the smallest enrollment setting (26.7% EER at N = 10). In a secondary ORL face verification case study (N = 5, repeated splits, 40 subjects), LDA on standardized raw pixels reaches a 1.47% mean EER, and a predefined 40-component PCA yields no improvement; a dimensionality sensitivity analysis shows that PCA-40 captures 81.21% of the training set variance on average, and that performance improves substantially through approximately 40–80 components, with relatively small changes thereafter. On the desktop CPU test platform, 99th-percentile single-query latency stays below 0.7 ms for every configuration, although isolated larger observations preclude a hard-real-time guarantee; together with serialized deployment-bundle sizes, these measurements make the accuracy–latency–storage trade-offs explicit. Overall, the findings characterize constraint-dependent trade-offs for the evaluated datasets and configurations rather than identifying a universally superior classifier.