# Fast Eye Detector Using Siamese Network for NIR Partial Face Images

arXiv: https://arxiv.org/abs/2202.10671 · HTML: https://mlsanity.com/paper/2202.10671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 62 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [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)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [S3FD: Single Shot Scale-invariant Face Detector](https://mlsanity.com/paper/1708.05237/) (2017)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://mlsanity.com/paper/1907.05047/) (2019)
- [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019)
- [Rendering of Eyes for Eye-Shape Registration and Gaze Estimation](https://mlsanity.com/paper/1505.05916/) (2015)
- [FaceX-Zoo: A PyTorch Toolbox for Face Recognition](https://mlsanity.com/paper/2101.04407/) (2021)
- [PFLD: A Practical Facial Landmark Detector](https://mlsanity.com/paper/1902.10859/) (2019)
- [Face Detection using Deep Learning: An Improved Faster RCNN Approach](https://mlsanity.com/paper/1701.08289/) (2017)
- [Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results](https://mlsanity.com/paper/1804.10275/) (2018)
- [Max-Margin Object Detection](https://mlsanity.com/paper/1502.00046/) (2015)
- [Facial Feature Point Detection: A Comprehensive Survey](https://mlsanity.com/paper/1410.1037/) (2014)
- [Real-time Pupil Tracking from Monocular Video for Digital Puppetry](https://mlsanity.com/paper/2006.11341/) (2020)
- [All-in-Focus Iris Camera With a Great Capture Volume](https://mlsanity.com/paper/2011.09908/) (2020)

## Abstract (arXiv)

This paper proposes a fast eye detection method that is based on a Siamese network for near infrared (NIR) partial face images. NIR partial face images do not include the whole face of a subject since they are captured using iris recognition systems with the constraint of frame rate and resolution. The iris recognition systems such as the iris on the move (IOTM) system require fast and accurate eye detection as a pre-process. Our goal is to design eye detection with high speed, high discrimination performance between left and right eyes, and high positional accuracy of eye center. Our method adopts a Siamese network and coarse to fine position estimation with a fast lightweight CNN backbone. The network outputs features of images and the similarity map indicating coarse position of an eye. A regression on a portion of a feature with high similarity refines the coarse position of the eye to obtain the fine position with high accuracy. We demonstrate the effectiveness of the proposed method by comparing it with conventional methods, including SOTA, in terms of the positional accuracy, the discrimination performance, and the processing speed. Our method achieves superior performance in speed.