.png){kind=icon}
返回
Morphing Attack Detection-Database, Evaluation Platform, and Benchmarking
.png){kind=icon}
OA
AI
DOI：10.1109/TIFS.2020.3035252.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.
Keyword:
Databases
Face recognition
Faces
Benchmark testing
Digital images
Neural networks
Biometrics
morphing attack detection
face recognition
vulnerability of biometric systems
期刊
IF:
8
论文数:
5.3K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
The FERET database and evaluation procedure for face-recognition algorithms人脸识别算法的FERET数据库和评估程序
PREAI
分享收藏
分享收藏
查看更多内容