Synface

Synface Roboflow Universe Synface: face recognition with synthetic data this is the pytorch implementation of our iccv 2021 paper synface: face recognition with synthetic data. haibo qiu, baosheng yu, dihong gong, zhifeng li, wei liu and dacheng tao. In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images.

Stream Synface Music Listen To Songs Albums Playlists For Free On We evaluated the performance of synface on syn 10k 50 again by expanding the data to increase the variety of blurring and illumination and found that synface improved the accuracy from 88.98 to 91.23% compared to lfw. Synface proposes the use of discofacegan for the synthesis of face images, a disentangled learning scheme that enables precise control of targeted face properties such as identity, pose. With the recent success of deep neural networks, remarkable progress has been achieved on face recognition. however, collecting large scale real world training data for face recognition has turned out to be challenging, especially due to the label noise and privacy issues. meanwhile, existing face recognition datasets are usually collected from web images, lacking detailed annotations on. Cross domain evaluation of synface and realface. analysis: as shown in table 1 and figure 3, the domain gap with a special focus on poor intra class variations of synthetic data contributes to the performance gap.

Synface Face Recognition With Synthetic Data Haibo Qiu With the recent success of deep neural networks, remarkable progress has been achieved on face recognition. however, collecting large scale real world training data for face recognition has turned out to be challenging, especially due to the label noise and privacy issues. meanwhile, existing face recognition datasets are usually collected from web images, lacking detailed annotations on. Cross domain evaluation of synface and realface. analysis: as shown in table 1 and figure 3, the domain gap with a special focus on poor intra class variations of synthetic data contributes to the performance gap. In this section, we introduce face recognition with syn thetic data, i.e., synface, and the overall pipeline is illus trated in figure 2. we first introduce deep face recognition using margin based softmax loss functions. In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images. In this section, we introduce face recognition with syn thetic data, i.e., synface, and the overall pipeline is illus trated in figure 2. we first introduce deep face recognition using margin based softmax loss functions. It introduces synface, a method using synthetic face images with mixup techniques to bridge the performance gap between synthetic and real data, demonstrating synthetic data's potential for enhancing face recognition models.