CROSS-AGE FACE VERIFICATION USING FACE AGING

Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we propose to use the active appearance model (AAM) and gradient orientation pyramid (GOP) feature representation for this problem. First, we use the AAM on the dataset and generate the AAM images; we then get the representation of gradient orientation on a hierarchical model, which is the appearance of GOP. When combined with a support vector machine (SVM), experimental results show that our approach has excellent performance on two public domain face aging datasets: FGNET and MORPH. Second, we compare the performance of the proposed methods with a number of related face verification methods; the results show that the new approach is more robust and performs better.

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FM2u-Net: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr42600.2020.00577 ◽

2020 ◽

Author(s):

Wenxuan Wang ◽

Yanwei Fu ◽

Xuelin Qian ◽

Yu-Gang Jiang ◽

Qi Tian ◽

...

Keyword(s):

Face Verification

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DDAT: Dual Domain Adaptive Translation for Low-Resolution Face Verification in the Wild

Pattern Recognition ◽

10.1016/j.patcog.2021.108107 ◽

2021 ◽

pp. 108107

Author(s):

Qianfen Jiao ◽

Rui Li ◽

Wenming Cao ◽

Jian Zhong ◽

Si Wu ◽

...

Keyword(s):

Face Verification ◽

Low Resolution ◽

In The Wild ◽

Dual Domain

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Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

Applied Sciences ◽

10.3390/app9122535 ◽

2019 ◽

Vol 9 (12) ◽

pp. 2535

Author(s):

Di Fan ◽

Hyunwoo Kim ◽

Jummo Kim ◽

Yunhui Liu ◽

Qiang Huang

Keyword(s):

Human Computer Interaction ◽

Video Surveillance ◽

State Of The Art ◽

Learning Task ◽

Face Verification ◽

Task Learning ◽

The World ◽

In The Wild ◽

And Gender ◽

Learning Architectures

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

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