MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment

TL;DR

MobileAgeNet is a lightweight, real-time facial age estimation model optimized for mobile devices, balancing accuracy, size, and inference speed with a reproducible training pipeline.

Contribution

The paper introduces MobileAgeNet, a compact, efficient age estimation framework with a novel training pipeline and deployment process suitable for mobile applications.

Findings

Achieves MAE of 4.65 years on UTKFace dataset

Maintains 14.4 ms inference latency on mobile devices

Uses 3.23 million parameters for competitive accuracy

Abstract

Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight age-regression framework that achieves an MAE of 4.65 years on the UTKFace held-out test set while maintaining efficient on-device inference with an average latency of 14.4 ms measured using the AI Benchmark application. The model is built on a pretrained MobileNetV3-Large backbone combined with a compact regression head, enabling real-time prediction on mobile devices. The training and evaluation pipeline is integrated into the NN LEMUR Dataset framework, supporting reproducible experimentation, structured hyperparameter optimization, and consistent evaluation. We employ bounded age regression together with a two-stage fine-tuning strategy to improve training stability and generalization. Experimental results show that MobileAgeNet achieves competitive accuracy with 3.23M parameters, and that the deployment pipeline from PyTorch training through ONNX export to TensorFlow Lite conversion - preserves predictive behavior without measurable degradation under practical on-device conditions. Overall, this work provides a practical, deployment-ready baseline for mobile-oriented facial age estimation.