A weakly supervised method for makeup-invariant face verification

Sun, Yeneng; Ren, Lejian; Wei, Zhen; Liu, Bin; Zhai, Yanlong; Liu, Si

doi:10.1016/j.patcog.2017.01.011

Cited by 27 publications

(16 citation statements)

References 23 publications

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“…Additionally, the hand-crafted feature has used other fields of image processing and computer vision such as [15]. [2,3,11,[16][17][18][19]. In [2], external features are injected into fully connected layer of a dCNN to achieve better verification accuracies across large age gaps.…”

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

“…In [2], external features are injected into fully connected layer of a dCNN to achieve better verification accuracies across large age gaps. A triplet deep network is presented in [11] to verify face images across makeup variations. Coupled autoencoders have been used in [16] for face recognition across aging variations.…”

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

“…A prominent example in this regard is makeup wearing face images datasets which are relatively smaller datasets. The existing deep models to makeup-invariant face verification studies mainly rely on transfer learning such [11] as or face synthesis [19] to cope the negative effects of facial cosmetics on verification accuracy. However, we believe that transfer learning and face synthesis approaches are not suitable to fight appearance variations.…”

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

“…To fight the shortcomings of hand-crafted features, deep learning methods have been proposed owing to their ability to learn high level discriminative features automatically. The authors in [11] suggested a weakly supervised method for makeup-invariant face verification. They used a pretrained CNN on Internet videos and fine-tuned small makeup datasets.…”

Section: Introductionmentioning

confidence: 99%

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Data Augmentation-Assisted Makeup-Invariant Face Recognition

Sajid

Ali

Dar

et al. 2018

Mathematical Problems in Engineering

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Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.

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Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Augmentation-Assisted Makeup-Invariant Face Recognition

Sajid

Ali

Dar

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…) A recurring theme in unsupervised learning is the use of self-(or meta-) supervision (Pathak et al, 2016;Larsson et al, 2016;Zhang et al, 2016;Doersch et al, 2015;Gao et al, 2016;Misra et al, 2016;Wang & Gupta, 2015). This refers to a network trained for a pretext (or proxy) task, which is not of direct interest, but significantly relates to the final high-level task, e.g., object detection, classification, and action recognition (Girshick, 2015;Simonyan & Zisserman, 2014;Sun et al, 2017;Gkioxari et al, 2015). Automatic image colorization (Larsson et al, 2016;Zhang et al, 2016) is a typical example of a pretext task; naturally colorizing grey images requires prior knowledge of natural image appearance.…”

Section: Introductionmentioning

confidence: 99%

OCEAN: Object-centric arranging network for self-supervised visual representations learning

Ham

Kim

et al. 2019

Expert Systems with Applications

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Disentanglement For Discriminative Visual Recognition

Liu

2021

Recognition and Perception of Images

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Recent successes of deep learning-based recognition rely on maintaining the content related to the main-task label. However, how to explicitly dispel the noisy signals for better generalization in a controllable manner remains an open issue. For instance, various factors such as identity-specific attributes, pose, illumination and expression affect the appearance of face images. Disentangling the identity-specific factors is potentially beneficial for facial expression recognition (FER). This chapter systematically summarize the detrimental factors as task-relevant/irrelevant semantic variations and unspecified latent variation. In this chapter, these problems are casted as either a deep metric learning problem or an adversarial minimax game in the latent space. For the former choice, a generalized adaptive (N+M)-tuplet clusters loss function together with the identity-aware hard-negative mining and online positive mining scheme can be used for identity-invariant FER. The better FER performance can be achieved by combining the deep metric loss and softmax loss in a unified two fully connected layer branches framework via joint optimization. For the latter solution, it is possible to equipping an end-to-end conditional adversarial network with the ability to decompose an input sample into three complementary parts. The discriminative representation inherits the desired invariance property guided by prior knowledge of the task, which is marginal independent to the task-relevant/irrelevant semantic and latent variations. The framework achieves top performance on a serial of tasks, including lighting, makeup, disguise-tolerant face recognition and facial attributes recognition. This chapter systematically summarize the popular and practical solution for disentanglement to achieve more discriminative visual recognition.

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A weakly supervised method for makeup-invariant face verification

Cited by 27 publications

References 23 publications

Data Augmentation-Assisted Makeup-Invariant Face Recognition

Data Augmentation-Assisted Makeup-Invariant Face Recognition

OCEAN: Object-centric arranging network for self-supervised visual representations learning

Disentanglement For Discriminative Visual Recognition

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