Importance of eyes and eyebrows for face recognition system

Radji, Nadjet; Cherifi, Dalila; Azrar, Arab

doi:10.1109/ceit.2015.7233088

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Only a few studies were published on the impact of deep learning-based face recognition. Radji et al (2015) analyzed the impact of eyebrows and eyes removal on traditional FR by blurring those areas. Hofer et al (2021) blacked out certain face areas (eyes, nose, and mouth) and analyzed how three state-of-the-art face detection methods (MTCNN, Retinaface, and DLIB) were affected.…”

Section: Introductionmentioning

confidence: 99%

The contribution of different face parts to deep face recognition

Lestriandoko

Veldhuis

Spreeuwers

2022

Front. Comput. Sci.

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The development of face recognition improvements still lacks knowledge on what parts of the face are important. In this article, the authors present face parts analysis to obtain important recognition information in a certain area of the face, more than just the eye or eyebrow, from the black box perspective. In addition, the authors propose a more advanced way to select parts without introducing artifacts using the average face and morphing. Furthermore, multiple face recognition systems are used to analyze the face component contribution. Finally, the results show that the four deep face recognition systems produce a different behavior for each experiment. However, the eyebrows are still the most important part of deep face recognition systems. In addition, the face texture played an important role deeper than the face shape.

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Section: Introductionmentioning

confidence: 99%

The contribution of different face parts to deep face recognition

Lestriandoko

Veldhuis

Spreeuwers

2022

Front. Comput. Sci.

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“…Moreover, for some certain applications (Juefei‐Xu & Savvides, ) when people's faces are occluded, such as in door access control systems in hospitals, doctors and nurses usually wear masks on their faces with eyebrows exposed. And for criminal or terrorist recognition, eyebrow recognition would be more helpful to recognize a criminal or terrorist who occludes his face with a scarf/veil/mask under his eyes (Radji, Cherifi, & Azrar, ). Therefore, eyebrow recognition would be an encouraging and promising field in biometric technology.…”

Section: Introductionmentioning

confidence: 99%

Eyebrow semantic description via clustering based on Axiomatic Fuzzy Set

Ren

et al. 2018

WIREs Data Min & Knowl

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In this paper, we aim to extract the eyebrow semantic descriptors based on the Axiomatic Fuzzy Set (AFS) theory. First, we normalize the image of the eyebrows and automatically mark it by using a recently proposed facial landmarks detector. Second, a recent clustering algorithm based on AFS theory for eyes semantics abstraction is used to cluster these detected eyebrow landmarks and give semantic descriptors for each eyebrow. Finally, BU-4DFE and Multi-PIE databases are used to validate the effectiveness of the proposed approach. Furthermore, the eyebrow descriptions with different expressions and similar expressions are investigated and we show that the semantic descriptors are closely related to expressions. The experimental results show that the eyebrow semantic concepts obtained by the AFS clustering algorithm are better than the results produced by the traditional clustering methods (k-means and FCM) in terms of consistency for different expressions.

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