Paper Reading AI Learner

Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator

2021-11-29 06:14:00
Usman Cheema, Mobeen Ahmad, Dongil Han, Seungbin Moon

Abstract

Visible-to-thermal face image matching is a challenging variate of cross-modality recognition. The challenge lies in the large modality gap and low correlation between visible and thermal modalities. Existing approaches employ image preprocessing, feature extraction, or common subspace projection, which are independent problems in themselves. In this paper, we propose an end-to-end framework for cross-modal face recognition. The proposed algorithm aims to learn identity-discriminative features from unprocessed facial images and identify cross-modal image pairs. A novel Unit-Class Loss is proposed for preserving identity information while discarding modality information. In addition, a Cross-Modality Discriminator block is proposed for integrating image-pair classification capability into the network. The proposed network can be used to extract modality-independent vector representations or a matching-pair classification for test images. Our cross-modality face recognition experiments on five independent databases demonstrate that the proposed method achieves marked improvement over existing state-of-the-art methods.

Abstract (translated)

URL

https://arxiv.org/abs/2111.14339

PDF

https://arxiv.org/pdf/2111.14339.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