Multimodal biometrics recognition using multi-layer fusion convolutional neural network with RGB and texture descriptor = 다중 융합 합성곱 네트워크와 컬러 및 질감 디스크립터를 이용한 멀티모달 생체인식

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Most existing deep learning architectures rely on feature concatenation or weights combination to construct a representation layer to recognize distinct image biometrics. Using a concatenation operator is inefficient, as it does not effectively cooperate with the multimodal data to achieve adequate recognition performance. Therefore, this motivates the need for a fusion algorithm. This dissertation proposes a novel approach by utilizing a multi-layer fusion algorithm with multimodal biometrics through facial and periocular modalities with texture descriptors. This approach discovers complementary information within the multimodal biometrics data. The multi-layer fusion algorithm is effective in multimodal scenarios, resulting in effective learning. We incorporate multi-layer fusion by correlating multimodal data with its texture descriptors. Through our experiments using public datasets, our proposed approach improved over baseline methods especially when subjected to environmental variations such as aging, illumination, poses, and occlusions. These results also proved that a flexible fusion approach could effectively mitigate the shortcomings of using raw data for identifying individuals, thereby improving on recognition performances across challenging datasets and unconstrained environments.
Advisors
Ro, Yong Manresearcher노용만researcher
Description
한국과학기술원 :글로벌IT기술대학원프로그램,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 글로벌IT기술대학원프로그램, 2019.2,[iv, 40 p. :]

Keywords

Multi-layer fusion convolutional neural network▼adual-stream convolutional neural network▼amultimodal biometrics recognition▼adeep multimodal learning▼atexture descriptor; 다중 융합 합성곱네트워크▼a듀얼스트림 합성곱네트워크▼a멀티모달 생체인식▼a심층 멀티모달 학습▼a질감 디스크립터

URI
http://hdl.handle.net/10203/264458
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=841706&flag=dissertation
Appears in Collection
ITP-Theses_Ph.D.(박사논문)
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