抗光影變化的人臉辨識技術

Robust Face Recognition for Varying Lighting Conditions

黃明揚(Ming-Yang Huang)

指導教授 ： 林慧珍

淡江大學/工學院/資訊工程學系碩士班/碩士(2019年)

https://doi.org/10.6846/TKU.2019.00036

摘要

一般來說，人臉影像光影變化會嚴重地影響到人臉辨識系統的辨識率。為了減少光照條件的影響，我們提出以樣本學習的方式為基礎的光線轉換技術。系統的目的是將測試影像透過CVAE模型轉換輸入影像的光照條件，目的是使光線條件與資料庫比對樣本影像的光照條件一致，並藉此提高辨識率。實驗結果顯示，我們的方法不需要手動標記人臉影像的陰影區域，給定一張測試人臉影像與一群人臉訓練資料集，系統會自動改變測試影像的光照條件。

關鍵字

條件式變分自編碼機 ； 孿生神經網路

並列摘要

In general, face recognition systems are not robust against illumination variation; in this study, we propose a learning-based framework to reconstruct a face in different lighting conditions in order to improve the performance of downstream applications such as automatic face recognition. The proposed framework is based on conditional variational autoencoder (CVAE) network to disentangle the identity and the shadow effect. Our proposed framework doesn’t need to label the shadow area of faces in any way. It is trained with a loss function to better preserve the identity and reconstruction result. YaleB face database with illumination variation are used for training and evaluation purpose. The experimental results show that the proposed framework can automatically remove shadows from a single image.

並列關鍵字

Conditional Variational Autoencoder(CVAE) ； Siamese Deep Neural Network

參考文獻

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