人臉辨識是經由擷取人臉影像，分析其臉部特徵來進行身分認證的一種技術，近年來基於深度學習運用於人臉辨識逐漸成為主流的研究方向，藉由輸入大量影像資料，解析其向素值排列之向量資訊，學習人臉特徵，最終達到可以識別人臉的目的。
使用MTCNN作為人臉檢測的部分，雖然其能夠穩定且精準地框選人臉，但是因為需要花費較大計算量，所以導致在檢測上的速度較為緩慢，進而使得整體系統效能受到影響。而在人臉識別的部分使用關係類神經網路架構，並且以一人一個模型的方式來增減辨識人數，雖然能夠對於每個人都能達到最佳的辨識度，但會在可辨識人數多的情況下，造成辨識效率降低的現象。
本論文旨在針對人臉檢測以及人臉識別的部分做改進， MTCNN方面透過改進現有架構的方式，使得人臉檢測速度加快。而在人臉識別方面使用了演算法改變模型搜尋的方式，使得在辨識人數多的狀況下，也能夠具有流暢的辨識速度，最終整合這兩部分來獲得執行效率高之人臉辨識系統。

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