# Grid Loss: Detecting Occluded Faces

arXiv: https://arxiv.org/abs/1609.00129 · HTML: https://mlsanity.com/paper/1609.00129/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2016.

## Built on by (most cited first, 17 of 17)

- [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021)
- [FocusFace: Multi-task Contrastive Learning for Masked Face Recognition](https://mlsanity.com/paper/2110.14940/) (2021)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [Masked Face Recognition: Human vs. Machine](https://mlsanity.com/paper/2103.01924/) (2021)
- [Attention-based Partial Face Recognition](https://mlsanity.com/paper/2106.06415/) (2021)
- [BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion](https://mlsanity.com/paper/2110.01179/) (2021)
- [Now You See Me: Robust approach to Partial Occlusions](https://mlsanity.com/paper/2304.11779/) (2023)
- [An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms](https://mlsanity.com/paper/2306.08549/) (2023)
- [Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement](https://mlsanity.com/paper/2103.02167/) (2021)
- [Towards robustness under occlusion for face recognition](https://mlsanity.com/paper/2109.09083/) (2021)
- [Analysis of face detection, face landmarking, and face recognition performance with masked face images](https://mlsanity.com/paper/2207.06478/) (2022)
- [Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach](https://mlsanity.com/paper/2112.15031/) (2021)
- [MaskMTL: Attribute prediction in masked facial images with deep multitask learning](https://mlsanity.com/paper/2201.03002/) (2022)
- [Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification](https://mlsanity.com/paper/2205.07203/) (2022)
- [A Comparative Study of Face Detection Algorithms for Masked Face Detection](https://mlsanity.com/paper/2305.11077/) (2023)
- [Mask-up: Investigating Biases in Face Re-identification for Masked Faces](https://mlsanity.com/paper/2402.13771/) (2024)

## Abstract (arXiv)

Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance.