臺灣博碩士論文加值系統

深度殘差網絡 (ResNet) 是圖像分類和物件偵測中最先進的架構之一，當網路架構中最後一層被移除時，它可以當作一個良好的特徵向量抽取器。將人臉轉換成特徵向量後，一些任務例如人臉識別、人臉驗證，就可以使用一些距離測量方法來實現。我們提出了一個基於ResNet特徵提取器的人臉識別框架，並加上其他改善性能的步驟，包括人臉偵測，人臉對齊，人臉驗證/識別以及透過近似最近鄰搜索 (ANNS) 來重新排序。首先，我們在三個常見的人臉檢測資料集上評估兩種人臉偵測演算法，MTCNN和FaceBoxes，接著總結這兩種方法的最佳使用場景。其次，經過特定的預處理和後處理，我們的系統選擇基於ResNet的特徵提取器，並在LFW資料集中達到99.33%的驗證準確度。第三，我們使用懲罰曲線來確定最佳配置並獲得良好的臉部驗證結果。最後，基於這篇論文提出的重新排序策略，我們的方法在大型類別間變異數據集 (在CASIA-faceV5資料集上提升1.47%，在CASIA-WebFace資料集上提高2.28%) 與大型類別內變異數據集 (在FG-NET資料集上提高1.3%，在CACD資料集上提高2.43%) 上都能使辨識率上升。

Deep residual network (ResNet) is one of the state-of-the-art architectures in image classification and object detection, which can serve as a robust feature extractor when the last layer is removed. Once the faces are embedded as feature vectors, tasks such as face recognition, verification and identification can be easily implemented using some distance measurements. This paper proposes a framework for face recognition based on feature extractor from ResNet, together with other steps for improving its performance, including face detection, face alignment, face verification/identification, and re-ranking via Approximate Nearest Neighbor Search (ANNS). First, we evaluate two face detection algorithms, MTCNN, and FaceBoxes on three common face detection benchmarks, and then summarize the best usage scenario for each approach. Second, with certain preprocessing and postprocessing, our system selects the ResNet-based feature extractor, which achieves 99.33% verification accuracy on LFW benchmark. Third, we use the penalty curve to determine the best configuration and obtain improved results of face verification. Lastly, based on the proposed re-ranking policy, our method not only boosts the accuracy in large inter-class variation datasets (1.47% and 2.28% improvement in rank-1 accuracy for CASIA-faceV5 and CASIA-WebFace respectively) but also in large intra-class variation datasets (1.3% and 2.43% improvement in rank-1 accuracy for FG-NET and CACD respectively).

口試委員會審定書 i
致謝 ii
摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Tables ix
1 Introduction 1
2 Related Work 5
2.1 Face Detection 5
2.2 Face Alignment 9
2.3 Face Identification and Verification 10
2.3.1 Face representation 11
2.3.2 Discriminative metric learning 12
2.4 Approximate Nearest Neighbor Search 16
2.5 Re-ranking for object retrieval 20
3 Methods 21
3.1 Face Detection 22
3.1.1 MTCNN 22
3.1.2 FaceBoxes 23
3.2 Face Alignment 24
3.3 Face Feature Extractor 26
3.4 Approximate Nearest Neighbors Search 27
3.5 Re-ranking Policy 29
4 Experiments 33
4.1 Evaluation of Face Detection 33
4.1.1 Benchmark evaluation 34
4.1.2 Face Detection Runtime Efficiency 36
4.1.3 Face Detection Conclusion 37
4.2 Evaluation of Face Verification 37
4.2.1 Performance on LFW 38
4.2.2 Performance on CASIA-FaceV5 and Helen dataset 39
4.2.3 Alignment and Average 41
4.3 Evaluation of Face Identification 43
4.3.1 Experiments on CASIA-FaceV5 44
4.3.2 Experiments on CASIA-WebFace 45
4.3.3 Experiments on FG-NET 47
4.3.4 Experiments on CACD 49
4.4 Parameter Analysis 51
5 Conclusions and Discussions 53
Reference 54

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