本論文提出人臉辨識點名系統，使用具有強健性之人臉偵測器，利用此人臉偵測器同時擷取多個人臉資訊，有校正與定位之功能。於辨識系統中，引用區域共變異數矩陣演算法，進行人臉辨識點名及身分驗證。 本系統引用區域共變異數矩陣演算法進行人臉辨識，為了降低影像資料，不完整之限制，而辨識受到阻礙，原圖劃分為6個區域，經過賈伯濾波器後得到人臉之主要特徵，例如五官(眼睛、耳朵、鼻子、嘴唇和眉毛)，因此提高辨識力與強健性。共變異數矩陣是一個對稱性矩陣，它的對角線項目代表每個特徵之變異量，而且非對角線項目代表它們分別的關聯性，樣本特徵矩陣與測試特徵矩陣計算出廣義的特徵值，比對出差距值最小即表示測試影像接近資料庫中已被認證之身分，顯示該認證身分資訊。 In this paper, we proposed a face recognition based presentation checking system with a robust human face detector. Utilize this detector which can not only extract several human faces information at the same time but the corrected and made a reservation. In the recognition, we use the Region covariance matrices to implement the human face recognition and identity verification.
In this system, we used Region covariance matrices (RCMs) to process human face recognition, because of avoiding the failure of recognition while the input image data is defected. We derive main characteristics of face images by Gabor Filter after dividing the original image as 6 regions, such as facial features (eye, ear, nose, lips and eyebrow). And this method apparently boost, the recognition rate and make our system much more robust. After processing Region covariance matrices, opposite angles of matrix save and present the variation of characteristics between the non- opposite angle. The total variation between the sample image and test image estimate the matrix find out the generalized eigenvalues firstly. Secondly, we find out a identification which is similar as the one from database by searching the minimum distance. Between input images and database. Finally we can find out the input datum’s identifications by gathering the proper samples similar with input datum which are in the bead of the queue.
