# Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

arXiv: https://arxiv.org/abs/2103.16760 · HTML: https://mlsanity.com/paper/2103.16760/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 85 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [A Survey on Periocular Biometrics Research](https://mlsanity.com/paper/1810.03360/) (2018)
- [Fine-Grained Age Estimation in the wild with Attention LSTM Networks](https://mlsanity.com/paper/1805.10445/) (2018)
- [Periocular Recognition Using CNN Features Off-the-Shelf](https://mlsanity.com/paper/1809.06157/) (2018)
- [MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices](https://mlsanity.com/paper/1811.11080/) (2018)
- [Gender-From-Iris or Gender-From-Mascara?](https://mlsanity.com/paper/1702.01304/) (2017)
- [SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms](https://mlsanity.com/paper/2007.08566/) (2020)
- [How Transferable are CNN-based Features for Age and Gender Classification?](https://mlsanity.com/paper/1610.00134/) (2016)
- [Sex-Classification from Cell-Phones Periocular Iris Images](https://mlsanity.com/paper/1812.11702/) (2018)
- [Relevant features for Gender Classification in NIR Periocular Images](https://mlsanity.com/paper/1904.12007/) (2019)
- [Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder](https://mlsanity.com/paper/1710.02856/) (2017)
- [Recognizing Gender from Human Facial Regions using Genetic Algorithm](https://mlsanity.com/paper/1712.01661/) (2017)
- [Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images](https://mlsanity.com/paper/1805.01912/) (2018)

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Periocular Biometrics: A Modality for Unconstrained Scenarios](https://mlsanity.com/paper/2212.13792/) (2022)
- [One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations](https://mlsanity.com/paper/2307.05128/) (2023)
- [Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults](https://mlsanity.com/paper/2110.01641/) (2021)
- [E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space](https://mlsanity.com/paper/2403.12197/) (2024)
- [OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation](https://mlsanity.com/paper/2505.05374/) (2025)
- [Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection](https://mlsanity.com/paper/2608.14701/) (2026)
- [Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation](https://mlsanity.com/paper/2610.06102/) (2026)

## Abstract (arXiv)

We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft-biometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pre-trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesize that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pre-training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition.