SCface – surveillance cameras face database

By Mislav Grgic, Kresimir Delac, Sonja Grgic. 2011

567 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Ming-Hsuan Yang
4. Chen Change Loy
5. Zhangyang Wang
6. Xiaogang Wang
7. Yi Yang
8. Chunhua Shen

In Signal Processing

1. Zhen Lei
2. Yong Xu
3. Stan Z. Li
4. Dong Yi
5. Hong Yan
6. Shifeng Zhang
7. Lin Gu
8. Shengwei Zhao

Citations in the corpus

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “These systems have applications in security such as authentication for electronic devices ( e.g ., unlocking smartphones) or monitoring surveillance CCTVs [14].”
• Very low resolution face recognition problem (2010, methodology): “The face images of SCface have very challenging illumination and pose variations, which cause the very low benchmark performance in which the recognition rate is ranging from 0.7% to 7.7% as reported in [19].”
• Improved Knowledge Distillation for Training Fast Low Resolution Face Recognition Model (2019, methodology): “SCFace is a widely-used benchmark for evaluation of LR face recognition engine [21].”
• Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation (2018, methodology): “73% with the baseline PCA model, showing face retrieval on SCface is a very challenging task.”
• Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, background): “A few works [64]–[66] make preliminary attempts for”
• Coupled Kernel Embedding for Low-Resolution Face Image Recognition (2012, methodology): “14, in which the accuracies labeled with PCA corresponding to the results of [39] present the state-of-the-art performance with the crossvalidated evaluation protocol.”
• Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks (2018, unknown): “Database scFace[25] Utrecht[26] own various”
• Studying Very Low Resolution Recognition Using Deep Networks (2016, background): “Dataset Although many face image datasets, such as LFW [13], SCface [11], and CMU-PIE [27], have been developed, most of them were collected in controlled indoor environments with limited variability.”