# Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition

arXiv: https://arxiv.org/abs/2111.01325 · HTML: https://mlsanity.com/paper/2111.01325/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [A Survey on Periocular Biometrics Research](https://mlsanity.com/paper/1810.03360/) (2018)
- [Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition](https://mlsanity.com/paper/1908.01790/) (2019)
- [Deep Hashing for Secure Multimodal Biometrics](https://mlsanity.com/paper/2012.14758/) (2020)
- [Face Attribute Prediction Using Off-the-Shelf CNN Features](https://mlsanity.com/paper/1602.03935/) (2016)
- [An Experimental Study of Deep Convolutional Features For Iris Recognition](https://mlsanity.com/paper/1702.01334/) (2017)
- [Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protection](https://mlsanity.com/paper/1908.02706/) (2019)
- [PF-cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild](https://mlsanity.com/paper/2005.02166/) (2020)
- [Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud](https://mlsanity.com/paper/1710.09183/) (2017)
- [Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding](https://mlsanity.com/paper/1902.04149/) (2019)
- [Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval](https://mlsanity.com/paper/2004.03378/) (2020)

## Built on by (most cited first, 5 of 5)

- [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)
- [Attention-Aware Transformer-Based Aggregation Network for Video Periocular Recognition](https://mlsanity.com/paper/2605.16550/) (2026)
- [Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection](https://mlsanity.com/paper/2608.14701/) (2026)

## Abstract (arXiv)

In recent years, periocular recognition has been developed as a valuable biometric identification approach, especially in wild environments (for example, masked faces due to COVID-19 pandemic) where facial recognition may not be applicable. This paper presents a new deep periocular recognition framework called attribute-based deep periocular recognition (ADPR), which predicts soft biometrics and incorporates the prediction into a periocular recognition algorithm to determine identity from periocular images with high accuracy. We propose an end-to-end framework, which uses several shared convolutional neural network (CNN)layers (a common network) whose output feeds two separate dedicated branches (modality dedicated layers); the first branch classifies periocular images while the second branch predicts softn biometrics. Next, the features from these two branches are fused together for a final periocular recognition. The proposed method is different from existing methods as it not only uses a shared CNN feature space to train these two tasks jointly, but it also fuses predicted soft biometric features with the periocular features in the training step to improve the overall periocular recognition performance. Our proposed model is extensively evaluated using four different publicly available datasets. Experimental results indicate that our soft biometric based periocular recognition approach outperforms other state-of-the-art methods for periocular recognition in wild environments.