本論文以使用市售網路攝影機搭配開放資源的電腦視覺函式庫OpenCV與深度學習技術，針對身分識別提出一套「基於卷積神經網路之身分識別系統」，預期應用於門禁、區域安全監視、廣告播放或其它需經由確認身分提升使用權限之相關系統。 本論文使用Python配合TensorFlow內建之GoogLeNet卷積神經網路模型為基礎，進行監督式學習以取得人臉圖像特徵並依身分分類，本論文使用自行整理的人臉圖像資料並實驗比較GoogLeNet三個版本的模型的辨識率，在以實驗中辨識率最高的神經網路架構，加入殘差網路實驗是否能提高辨識率。以上述最佳辨識率之神經網路模型，利用OpenCV載入影片即時識別影片中人物身分以驗證本研究訓練的神經網路模型之實用性。 結果驗證部分，本論文在自行整理的14位公眾人物，每位公眾人物至少130張人臉圖像做為訓練及測試與驗證樣本，其中最佳辨識率的神經網路在1260張的圖像訓練樣本辨識率為100%，450張的圖像驗證樣本辨識率為99.11%，即時影像部分從取得影片中的人臉圖片到完成身分識別時間約為0.1秒。 ;This paper proposes a set of "CNN-based identity recognition system" for identity recognition using a computer vision library OpenCV and deep learning technology and webcam. It is expected to be applied to access control and regional security. Monitoring, advertising, or other related systems that need to be enhanced by confirming their identity. This thesis is based on Python and TensorFlow′s built-in GoogLeNet CNN model. Supervised learning is used to obtain facial image features and classified by identity. This paper uses self-organizing face image data and compares GoogLeNet. The identification rate of the three versions of the model, in the neural network architecture with the highest recognition rate in the experiment, can increase the recognition rate by adding the residual network experiment. Using the neural network model of the above-mentioned best recognition rate, the OpenCV is used to load the movie to instantly recognize the character in the film to verify the practicability of the neural network model of the research training. In the verification part of the results, the paper has self-organized 14 public figures, and each public figure has at least 130 face images as training and test and verification samples, among which the best recognition rate of the neural network is in 1260 images. The recognition rate of the training sample is 100%, and the image recognition rate of the 450 images is 99.11%. The time of the instant image recognition from the face image in the film to the completion identity is about 0.1 second.
