# Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning

arXiv: https://arxiv.org/abs/2302.11102 · HTML: https://mlsanity.com/paper/2302.11102/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 59 references that are in the map.
- First posted on arXiv 22 Feb 2023.

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

- [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)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://mlsanity.com/paper/2104.05833/) (2021)
- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation](https://mlsanity.com/paper/2012.07791/) (2020)
- [Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach](https://mlsanity.com/paper/1706.00906/) (2017)
- [How Does Gender Balance In Training Data Affect Face Recognition Accuracy?](https://mlsanity.com/paper/2002.02934/) (2020)
- [MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes](https://mlsanity.com/paper/1603.07027/) (2016)
- [MAAD-Face: A Massively Annotated Attribute Dataset for Face Images](https://mlsanity.com/paper/2012.01030/) (2020)
- [Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology](https://mlsanity.com/paper/2112.14656/) (2021)
- [Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem](https://mlsanity.com/paper/2206.01881/) (2022)
- [PANDA: Pose Aligned Networks for Deep Attribute Modeling](https://mlsanity.com/paper/1311.5591/) (2013)
- [Deep Attributes Driven Multi-Camera Person Re-identification](https://mlsanity.com/paper/1605.03259/) (2016)
- [Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No](https://mlsanity.com/paper/1911.06396/) (2019)
- [The Gender Gap in Face Recognition Accuracy Is a Hairy Problem](https://mlsanity.com/paper/2206.04867/) (2022)
- [Consistency and Accuracy of CelebA Attribute Values](https://mlsanity.com/paper/2210.07356/) (2022)
- [A Deep Cascade Network for Unaligned Face Attribute Classification](https://mlsanity.com/paper/1709.03851/) (2017)
- [Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data](https://mlsanity.com/paper/1604.06433/) (2016)
- [Transferring a Semantic Representation for Person Re-Identification and Search](https://mlsanity.com/paper/1706.03725/) (2017)
- [A Survey of Deep Facial Attribute Analysis](https://mlsanity.com/paper/1812.10265/) (2018)
- [Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Prediction](https://mlsanity.com/paper/2108.04353/) (2021)

## Built on by (most cited first, 15 of 15)

- [What Should Be Balanced in a "Balanced" Face Recognition Dataset?](https://mlsanity.com/paper/2304.09818/) (2023)
- [FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs](https://mlsanity.com/paper/2503.21457/) (2025)
- [Beard Segmentation and Recognition Bias](https://mlsanity.com/paper/2308.15740/) (2023)
- [Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification](https://mlsanity.com/paper/2309.04447/) (2023)
- [Can the accuracy bias by facial hairstyle be reduced through balancing the training data?](https://mlsanity.com/paper/2405.20062/) (2024)
- [Hairmony: Fairness-aware hairstyle classification](https://mlsanity.com/paper/2410.11528/) (2024)
- [Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation](https://mlsanity.com/paper/2508.10672/) (2025)
- [LogicNet: A Logical Consistency Embedded Face Attribute Learning Network](https://mlsanity.com/paper/2311.11208/) (2023)
- [Goldilocks Test Sets for Face Verification](https://mlsanity.com/paper/2405.15965/) (2024)
- [Impact of Sunglasses on One-to-Many Facial Identification Accuracy](https://mlsanity.com/paper/2412.05721/) (2024)
- [FaceInsight: A Multimodal Large Language Model for Face Perception](https://mlsanity.com/paper/2504.15624/) (2025)
- [On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes](https://mlsanity.com/paper/2501.12020/) (2025)
- [What's color got to do with it? Face recognition in grayscale](https://mlsanity.com/paper/2309.05180/) (2023)
- [Vec2Face+ for Face Dataset Generation](https://mlsanity.com/paper/2507.17192/) (2025)
- [Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encoding](https://mlsanity.com/paper/2512.08981/) (2025)

## Abstract (arXiv)

Face attribute research has so far used only simple binary attributes for facial hair; e.g., beard / no beard. We have created a new, more descriptive facial hair annotation scheme and applied it to create a new facial hair attribute dataset, FH37K. Face attribute research also so far has not dealt with logical consistency and completeness. For example, in prior research, an image might be classified as both having no beard and also having a goatee (a type of beard). We show that the test accuracy of previous classification methods on facial hair attribute classification drops significantly if logical consistency of classifications is enforced. We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes. Using an attribute classifier trained on FH37K, we investigate how facial hair affects face recognition accuracy, including variation across demographics. Results show that similarity and difference in facial hairstyle have important effects on the impostor and genuine score distributions in face recognition. The code is at https:// github.com/ HaiyuWu/ LogicalConsistency.