Paper Reading AI Learner

Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning

2023-02-22 02:49:21
Haiyu Wu, Grace Bezold, Aman Bhatta, Kevin W. Bowyer

Abstract

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.

Abstract (translated)

面部特征研究目前仅使用简单的二进制特征对面部毛发进行分类,例如胡须/无胡须。我们创造了一个新的更详细的面部毛发标注方案,并将其应用于创建一个新的面部毛发属性数据集FH37K。面部特征研究还尚未处理逻辑一致性和完整性。例如,在先前的研究中,一个图像可能被分类为既有无胡须又有 goatee(一种胡须类型)。我们表明,如果逻辑一致性的分类结果得以强制,先前的面部毛发属性分类方法的性能将大幅下降。我们提出了逻辑一致性预测损失LCPLoss,以帮助学习跨属性的逻辑一致性,并提出了标签补偿训练策略,以消除一组相关属性中不存在积极预测的问题。使用训练在FH37K上的 attribute classifier,我们研究面部毛发对面部识别准确性的影响,包括年龄组的变化。结果表明,面部毛发的发型相似性和差异对人脸识别中的冒牌者和真实得分分布具有重要的影响。

URL

https://arxiv.org/abs/2302.11102

PDF

https://arxiv.org/pdf/2302.11102.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot