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Auflistung nach Autor:in "Chen, Cong"

1 - 4 von 4
  • Konferenzbeitrag
    The effect of face morphing on face image quality
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Fu, Biying; Spiller, Noémie; Chen, Cong; Damer, Naser
    Face morphing poses high security risk, which motivates the work on detection algorithms, as well as on anticipating novel morphing approaches. Using the statistical and perceptual image quality of morphed images in previous works has shown no clear correlation between the image quality and the realistic appearance. This motivated our study on the effect of face morphing on image quality and utility, we, therefore, applied eight general image quality metrics and four facespecific image utility metrics. We showed that MagFace (face utility metric) shows a clear difference between the bona fide and the morph images, regardless if they were digital or re-digitized. While most quality and utility metrics do not capture the artifacts introduced by the morphing process. Acknowledged that morphing artifacts are more apparent in certain areas of the face, we further investigated only these areas, for instance, tightly cropped face, nose, eyes, and mouth regions. We found that especially close to the eyes and the nose regions, using general image quality metrics as MEON and dipIQ can capture the image quality deterioration introduced by the morphing process.
  • Konferenzbeitrag
    The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Damer, Naser; Grebe, Jonas Henry; Chen, Cong; Boutros, Fadi; Kirchbuchner, Florian; Kuijper, Arjan
    Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases.We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.
  • Konferenzbeitrag
    On the assessment of face image quality based on handcrafted features
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Henniger, Olaf; Fu, Biying; Chen, Cong
    This paper studies the assessment of the quality of face images, predicting the utility of face images for automated recognition. The utility of frontal face images from a publicly available dataset was assessed by comparing them with each other using commercial off-the-shelf face recognition systems. Multiple face image features delineating face symmetry and characteristics of the capture process were analysed to find features predictive of utility. The selected features were used to build system-specific and generic random forest classifiers.
  • Konferenzbeitrag
    The relative contributions of facial parts qualities to the face image utility
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Fu, Biying; Chen, Cong; Henniger, Olaf; Damer, Naser
    Face image quality assessment predicts the utility of a face image for automated face recognition. A high-quality face image can achieve good performance for the identification or verification task. Some recent face image quality assessment algorithms are established on deep-learningbased approaches, which rely on face embeddings of aligned face images. Such face embeddings fuse complex information into a single feature vector and are, therefore, challenging to disentangle. The semantic context however can provide better interpretable insights into neural-network decisions. We investigate the effects of face subregions (semantic contexts) and link the general image quality of face subregions with face image utility. The evaluation is performed on two difficult largescale datasets (LFW and VGGFace2) with three face recognition solutions (FaceNet, SphereFace, and ArcFace). In total, we applied four face image quality assessment methods and one general image quality assessment method on four face subregions (eyes, mouth, nose, and tightly cropped face region) and the aligned faces. In addition, the effect of fusion of different face subregions was investigated to increase the robustness of the outcomes