Mobile face authentication for digital financial services must simultaneously satisfy recognition accuracy, computational efficiency, and biometric security under resource-constrained deployment conditions. This study proposes and evaluates a lightweight face-authentication framework that integrates detector–recognition pipeline optimization, protected biometric-template transformation, and blockchain-backed integrity support. Five face detection–recognition pipelines were systematically evaluated using a shared LightCNN-29v2 backbone fine-tuned on the Indonesian Muslim Student Face Dataset (IMSFD), with Mahalanobis-based Distance-Based Encryption (DBE) providing protected template matching and blockchain hash anchoring serving as an architectural integrity layer. Experiments on 3,660 images from 68 identities demonstrate that the MTCNN + LightCNN pipeline achieves the most favorable in-domain performance, reaching 94.95% accuracy, a ROC-AUC of 0.9970, an F1-score of 0.95, and successful processing of 3,546 out of 3,660 test images with an overall model size of approximately 5 MB. Applying Mahalanobis-based DBE further improves verification performance on IMSFD, increasing accuracy to 96.88% while reducing the False Acceptance Rate (FAR) from 0.83% to 0.18% and the False Rejection Rate (FRR) from 16.44% to 10.12%. Cross-dataset evaluation on LFW, CFP-FF, CFP-FP, AgeDB-30, CALFW, and CPLFW indicates that the proposed framework generalizes well to frontal-domain benchmarks but exhibits expected performance degradation under cross-pose and cross-age conditions due to distribution shift. Overall, the results demonstrate that detector selection is the dominant factor influencing end-to-end verification performance, while domain-specific fine-tuning and protected template matching are essential for secure and practical deployment in mobile financial authentication systems.