Open AccessMakeUpMirror: mirroring make‐ups and verifying faces post make‐up
Author(s) -
Banerjee Samik,
Das Sukhendu
Publication year - 2018
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2017.0265
Subject(s) - mirroring , computer science , face (sociological concept) , artificial intelligence , natural language processing , information retrieval , data science , world wide web , human–computer interaction , linguistics , sociology , philosophy , communication
Facial make‐up changes the appearance of a person and significantly degrades the performance of automated face verification (FV) systems. Here, the authors propose the design of an end‐to‐end siamese convolutional neural network (SCNN) that simultaneously replicates the facial make‐up of a subject using its target image (under facial make‐up) on a query face image and verifies the identity of the query face sample either with or without make‐up. The SCNN model is designed using loss functions to deal with the variations due to make‐up. The proposed architecture can reciprocate the make‐up at appropriate locations of the face without any human interventions. Rigorous experimentations on four benchmark facial make‐up datasets reveal the efficiency of their proposed model. Ablation studies show improvement of 4% for genuine acceptance rate at 0.1% false acceptance rate and reduction of equal error rate by 42% for FV in case of YouTube Make‐up dataset, and ‘10%’ in case of Virtual Make‐up dataset, when compared to the nearest state‐of‐the‐art method. For the transfer of make‐up, the similarity measures also show the effectiveness of their method, where the peak signal‐to‐noise ratio and structural similarity values show an improvement by ∼20–24 and ∼29–32%, respectively, when compared to a recent state‐of‐the‐art technique.