Towards Data-Efficient Computer Vision for Consumer-Grade Sensing: A Unified Framework for Skin Analysis, Biometrics, and Thermal Imaging

Computer vision systems deployed on consumer and handheld sensors (smartphones, low-cost thermal cameras, and compact clinical imaging devices) face three recurring technical challenges: (i) limited labeled data, (ii) domain shift across devices and capture conditions, and (iii) tight constraints on latency, memory, and energy. This paper presents a unified scientific framework for data-efficient image understanding spanning (a) skin-region segmentation and skin-condition classification, (b) palmprint-based authentication on mobile devices, and (c) thermal-image-based attribute classification. We synthesize a consistent training and evaluation protocol combining transfer learning, self-supervised pretraining, strong augmentation, metric learning, calibration, and synthetic data generation using modern generative models. To rigorously quantify the impact of these components under varying data regimes, we provide a formal sensitivity analysis that models the full protocol and demonstrates how sample size, augmentation strength, and pretraining choice affect segmentation overlap, verification error, and calibration. The result is a practical blueprint for building robust consumer-sensor vision systems under data scarcity, alongside a transparent methodology for benchmarking.