輕量化卷積神經網路的少樣本人臉辨識;Few-shot Face Recognition based on A Lightweight Convolutional Neural Network

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Title:	輕量化卷積神經網路的少樣本人臉辨識;Few-shot Face Recognition based on A Lightweight Convolutional Neural Network
Authors:	林宸嘉;Lin, Chen-Jia
Contributors:	資訊工程學系在職專班
Keywords:	少樣本
Date:	2021-07-28
Issue Date:	2021-12-07 12:53:21 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著科技進步，生活自動化的需求越來越普及，人臉辨識 (face recognition) 儼然也成為各種自動化應用中不可或缺的一部份，並且已被廣泛地應用在多種不同場域中。近年來，在深度學習的蓬勃發展帶動下，為了追求更高的辨識準確度，人臉辨識也大量引入深度學習的技術。在大量深度學習模式的評比下，為了更進一步提升準確度，網路模型的複雜度也一再被提升，網路階層變得更深、特徵數量變得更多，硬體規格的需求也一再增加。另一方面，由於隱私權的問題，人臉照片取得不易。鑒於前述問題，促使我們提出輕量化卷積神經網路的少樣本人臉辨識系統，主要研究內容就是如何在輕量化模型與少樣本的學習情形下還能提高人臉辨識的準確率 (accuracy)。本論文主要內容分為兩部份：第一部份為人臉辨識的網路架構，以MobileFaceNet 為骨幹並做進一步的輕量化，移除最後的升維 11 卷積層，藉此減少網路的模型大小，並加上通道注意力的 ECA-Net 模組 (module)，能夠在輕量化卷積神經網路的同時，還能保有準確率；並以此 ECA-MobileFaceNet-S 模型作為特徵擷取器，將人臉照片輸入此模型得到特徵向量。之後根據餘弦相似度 (cosine similarity) 做比對分類。第二部份為少樣本學習，為了改善少樣本學習的問題，使用增大角度邊界間距的 ProtoNets 訓練方法取代原先平方歐式距離的 Prototypical Networks 訓練方法，以達到特徵擷取器能讓 “群內聚集、群間分離” 的效果，以提升人臉辨識能力。我們的輕量化網路模型參數量比 MobileFaceNet 網路減少了 13.39%；在增大角度邊界間距的 ProtoNets 訓練方法與 large angular margin loss 的效果下，相比於原先平方歐式距離的 Prototypical Networks 訓練方法，在 LFW 資料集上的準確度提高了 10.79%。;With the progression of technology, the demand for life automation has become more and more popular, and face recognition has become an indispensable part of various automation applications and has been widely used in many different fields. In recent years, the development of deep learning is booming, in order to pursue the accuracy, face recognition has also introduced a large number of deep learning techniques. The network models were modified deeper and deeper, the number of parameters were continuously increased, and the requirements for hardware specifications were then upgraded again and again. On the other hand, it is hard to obtain face photos due to the privacy issues. To solve the problems, we propose a few-shot face recognition system based on a lightweight convolutional neural network, and mainly study how to improve the accuracy of face recognition in the case of lightweight models and few-shot learning. At first, we take MobileFaceNet as the backbone and remove the final 11 convolutional layer, thereby reducing the model size, and added ECA-Net modules to maintaining the accuracy. We call this new model ECA-MobileFaceNet-S. We can input face photos into the model to obtain the feature vectors, and then classify according to their cosine similarity. Then, to improve the few-shot learning problems, we refer to Prototypical Networks and replace the square Euclidean distance with large angular margin loss to improve the face recognition ability. We reduce the parameters of the original MobileFaceNet by 13.39%. Compared to the Prototypical Networks, the accuracy on the LFW dataset is improved by 10.79% under the effect of ProtoNets training method and the large angular margin loss.
Appears in Collections:	[Executive Master of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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