# Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss

arXiv: https://arxiv.org/abs/1707.07923 · HTML: https://mlsanity.com/paper/1707.07923/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

## Built on by (most cited first, 9 of 9)

- [DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification](https://mlsanity.com/paper/2112.04016/) (2021)
- [Segmentation-Reconstruction-Guided Facial Image De-occlusion](https://mlsanity.com/paper/2112.08022/) (2021)
- [ID-Booth: Identity-consistent Face Generation with Diffusion Models](https://mlsanity.com/paper/2504.07392/) (2025)
- [Locality-aware Channel-wise Dropout for Occluded Face Recognition](https://mlsanity.com/paper/2107.09270/) (2021)
- [Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers](https://mlsanity.com/paper/2311.02803/) (2023)
- [Impact of Facial Tattoos and Paintings on Face Recognition Systems](https://mlsanity.com/paper/2103.09939/) (2021)
- [A Comprehensive Study on Occlusion Invariant Face Recognition under Face Mask Occlusion](https://mlsanity.com/paper/2201.09089/) (2022)
- [Learning Representations for Masked Facial Recovery](https://mlsanity.com/paper/2212.14110/) (2022)
- [Bilinear-Convolutional Neural Network Using a Matrix Similarity-based Joint Loss Function for Skin Disease Classification](https://mlsanity.com/paper/2406.00696/) (2024)

## Abstract (arXiv)

Despite the recent success of convolutional neural networks for computer vision applications, unconstrained face recognition remains a challenge. In this work, we make two contributions to the field. Firstly, we consider the problem of face recognition with partial occlusions and show how current approaches might suffer significant performance degradation when dealing with this kind of face images. We propose a simple method to find out which parts of the human face are more important to achieve a high recognition rate, and use that information during training to force a convolutional neural network to learn discriminative features from all the face regions more equally, including those that typical approaches tend to pay less attention to. We test the accuracy of the proposed method when dealing with real-life occlusions using the AR face database. Secondly, we propose a novel loss function called batch triplet loss that improves the performance of the triplet loss by adding an extra term to the loss function to cause minimisation of the standard deviation of both positive and negative scores. We show consistent improvement in the Labeled Faces in the Wild (LFW) benchmark by applying both proposed adjustments to the convolutional neural network training.