近年來生物辨識廣泛的運用在身份驗證上，其好處在於每個人皆擁有獨一無
二的生理特徵，透過學習不同的特徵可以有效的區分個體。而人臉辨識系統基
於生物辨識的基礎下，透過類神經網路去學習不同人臉間的特徵差異後，可以
快速且準確地識別身分。
由於傳統的人臉辨識系統使用的人臉偵測架構快速，但偵測結果不穩定使辨
識結果受到影響，因此本論文欲使用穩定的人臉偵測架構使其擷取人臉的範圍
一致不會有誤判的情形，以及在資料量不足的情況下也透過使用資料增量產生
豐富的訓練資料，讓類神經網路可以有效的學習。
本實驗比較不同的偵測法則也證實使用 Multi-Task CNN 確實可以讓人臉辨
識系統在實際應用的場合上更加穩定，而資料增量使用模擬光影變化的作法，
使得影像可以學習光源分布的情形，透過使用 Multi-Task CNN 和資料增量來實
作人臉辨識系統，以降低光線對其所造成的影響。

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