李忠謀Lee, Chung-Mou許景皓Hsu, Ching-Hao2022-06-082020-11-102022-06-082020https://etds.lib.ntnu.edu.tw/thesis/detail/f04b45927ae19acbdaeb15970d7ff5e4/http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/117288生物辨識中,原先是由執法單位用於辨認身分,而人臉辨識中的人臉驗證是最不與主體接出且最能秘密進行,一旦取得人臉即能分析並進行認證,此模式已逐漸應用各個場域之中。 以往的人臉驗證研究,訓練模型通常使用大量的訓練資料建立模型,在進行人臉驗證之研究及評估,雖然大量訓練資料有助於穩定及提升人臉驗證辨識率,但較少人著墨於使用少量人臉能判斷人臉驗證之效期,因此本研究探討使用少量人臉資料建立模型並進行人臉驗證研究。 本研究使用少量的人臉,並從人臉照片中取得人臉特徵進行訓練模型,並透過支持向量機(Support Vector Machine,SVM)分別於每個人訓練模型,透過建立人臉模型以進行人臉驗證之方式,提高人臉驗證的表現。 透過本研究所使用支持向量機於不同的訓練資料下建立模型,並以 10 張人臉照片建立支持向量機模型,於人臉驗證中能維持二十一週 80%的辨識率。  In biometric identification, Face Verification were originally used to identify identities by law enforcement agencies. Face Verification can avoid contact with the subjects and complete the tasks privately. Once a face profile is obtained, it can be analyzed and authenticated. This method has been gradually applied in various fields. In the past, Face Verification often used huge amount of data to verify face profiles. However, although –using lots of data can improve the stability and recognizability of Face Verification, it can ralely use a small amount of face profile data to complete Face Verification tasks. Hence, this research aimed to use –a small amount of face data to build a Face Verification mechanism. This research proposed a method to extract face features by using feature learning and built the Support Vector Machine (SVM) model to verify a pair of facial images that belong to the same or different subjects. According to research, we can use within 10 face data to train a face model. The result shows that it can maintain an 80% recognition rate in 21 weeks.  人臉驗證少量資料支持向量機Face verificationScarce dataSupport vector machine學術論文