DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild
This paper describes our approach for the Disguised Faces in the Wild (DFW) 2018 challenge. The task here is to verify the identity of a person among disguised and impostors images. Given the importance of the task of face verification it is essential to compare methods across a common platform. Our approach is based on VGG-face architecture paired with Contrastive loss based on cosine distance metric. For augmenting the data set, we source more data from the internet. The experiments show the effectiveness of the approach on the DFW data. We show that adding extra data to the DFW dataset with noisy labels also helps in increasing the generalization performance of the network. The proposed network achieves 27.13% absolute increase in accuracy over the DFW baseline.
Code (1)
Tasks
Disguised Face VerificationFace VerificationSimilar Papers 제목 키워드 기반
A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
Facial recognition has always been a challeng- ing task for computer vision scientists and experts. Despite complexities arising due to variations in camera parameters, illumination and face orientations, significant pro…
Face IdentificationFace RecognitionRecognizing Disguised Faces in the Wild
Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, the current face recognition systems achie…
Disguised Face VerificationFace RecognitionOn Matching Faces with Alterations due to Plastic Surgery and Disguise
Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The state-of-art deep learning models are not sufficiently successful due to the availability of limited training s…
Face RecognitionLarge age-gap face verification by feature injection in deep networks
This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/o…
Age-Invariant Face RecognitionFace RecognitionFace VerificationDFKI-Speech System for WildSpoof Challenge: A robust framework for SASV In-the-Wild
This paper presents the DFKI-Speech system developed for the WildSpoof Challenge under the Spoofing aware Automatic Speaker Verification (SASV) track. We propose a robust SASV framework in which a spoofing detector and a…
Speaker VerificationGraph Neural Network