Illumination Normalization via Merging Locally Enhanced Textures for Robust Face Recognition
In order to improve the accuracy of face recognition under varying illumination conditions, a local texture enhanced illumination normalization method based on fusion of differential filtering images (FDFI-LTEIN) is proposed to weaken the influence caused by illumination changes. Firstly, the dynamic range of the face image in dark or shadowed regions is expanded by logarithmic transformation. Then, the global contrast enhanced face image is convolved with difference of Gaussian filters and difference of bilateral filters, and the filtered images are weighted and merged using a coefficient selection rule based on the standard deviation (SD) of image, which can enhance image texture information while filtering out most noise. Finally, the local contrast equalization (LCE) is performed on the fused face image to reduce the influence caused by over or under saturated pixel values in highlight or dark regions. Experimental results on the Extended Yale B face database and CMU PIE face database demonstrate that the proposed method is more robust to illumination changes and achieve higher recognition accuracy when compared with other illumination normalization methods and a deep CNNs based illumination invariant face recognition method
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionRobust Face RecognitionSimilar Papers 제목 키워드 기반
SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization
Artistic style transfer aims to transfer the style characteristics of one image onto another image while retaining its content. Existing approaches commonly leverage various normalization techniques, although these face …
Style TransferRecasting Regional Lighting for Shadow Removal
Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint …
ObjectShadow RemovalLearning to Decouple the Lights for 3D Face Texture Modeling
Existing research has made impressive strides in reconstructing human facial shapes and textures from images with well-illuminated faces and minimal external occlusions. Nevertheless, it remains challenging to recover ac…
Vanishing Feature: Diagnosing Model Merging and Beyond
Model merging offers an efficient way to combine pre-trained neural networks but often suffers from inconsistent performance, especially when merging models with different initializations. We identify the ``vanishing fea…
Linear Mode ConnectivitymodelRe-basinExperimenting with Normalization Layers in Federated Learning on non-IID scenarios
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of data…
Federated LearningPrivacy Preserving