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Journal Article 딥러닝 기반 고성능 얼굴 인식 기술 동향
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Authors
김형일, 문진영, 박종열
Issue Date
2018-08
Citation
전자통신동향분석, v.33, no.4, pp.45-53
ISSN
1225-6455
Publisher
한국전자통신연구원 (ETRI)
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.22648/ETRI.2018.J.330405
Abstract
As face recognition (FR) has been well studied over the past decades, FR technology has been applied to many real-world applications such as surveillance and biometric systems. However, in the real-world scenarios, FR performances have been known to be significantly degraded owing to variations in face images, such as the pose, illumination, and low-resolution. Recently, visual intelligence technology has been rapidly growing owing to advances in deep learning, which has also improved the FR performance. Furthermore, the FR performance based on deep learning has been reported to surpass the perfor-mance level of human perception. In this article, we discuss deep-learning based high-performance FR technologies in terms of representative deep-learning based FR architectures and recent FR algorithms robust to face image variations (i.e., pose-robust FR, illumination-robust FR, and video FR). In addition, we investigate big face image datasets widely adopted for performance evaluations of the most recent deep-learning based FR algorithms.
KSP Keywords
Biometric system, Face Image, High performance, Image datasets, Performance evaluation, Real-world applications, deep learning(DL), face recognition, human perception, illumination-robust, low resolution
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: