지문 영상 잡음 제거 및 복원을 위한 심층 합성곱 신경망

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2021. 2. 강명주.Biometric authentication using fingerprints requires a method for image denoising and inpainting to extract fingerprints from degraded fingerprint images. A few deep learning models for fingerprint image denoising and inpainting were proposed in ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018. In this thesis, a new deep learning model for fingerprint image denoising is proposed. The proposed model is adapted from FusionNet, which is a convolutional neural network based deep learning model for image segmentation. The performance of the proposed model was demonstrated using the dataset from the ECCV 2018 ChaLearn Competition. It was shown that the proposed model obtains better results compared with the models that achieved high performances in the competition.지문을 사용한 생체 인식 인증은 품질이 저하된 지문 영상에서 지문을 추출하기 위한 영상 잡음 제거 및 복원 방법을 필요로 한다. 지문 영상 잡음 제거 및 복원을 위한 몇 가지 딥러닝 모델이 ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018에서 제안되었다. 본 논문에서는 지문 영상 잡음 제거를 위한 새로운 딥러닝 모델을 제안한다. 제안된 모델은 영상 분할을 위한 합성곱 신경망 기반 딥러닝 모델인 FusionNet을 수정하여 작성하였다. 제안된 모델의 성능은 ChaLearn Competition의 데이터셋을 사용하여 검증되었다. 이를 통해 제안된 모델이 대회에서 높은 성능을 획득한 다른 모델들에 비하여 더 나은 결과를 얻음을 확인하였다.Abstract i Contents ii 1 Introduction 1 2 Related Work 3 2.1 Residual Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Convolutional Neural Networks for Semantic Segmentation . . . . . . 4 2.2.1 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.2 FusionNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Recent Trends in Fingerprint Image Denoising . . . . . . . . . . . . . 6 3 Proposed Model 7 3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Architecture Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.1 Residual Block . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.3 Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.4 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Experiments 13 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.1 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.2 Comparison with Other Models . . . . . . . . . . . . . . . . 17 5 Conclusion 21 Abstract (In Korean)Maste