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MakeUpMirror: mirroring make-ups and verifying faces post make-up

MakeUpMirror: mirroring make-ups and verifying faces post make-up

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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.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2017.0265
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