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蔡志順 (Jason Tsai) 2020/5/29
@IEAT會議中心 台灣人權促進會
基於深度學習的
人臉辨識技術簡介
Copyright Notice:
All figures in this presentation are taken from
miscellaneous sources and their copyrights
belong to the original authors. This
presentation itself adopts Creative Commons
license.
人臉辨識方法
人臉辨識範疇
 人臉檢測 (Face Detection)
 人臉表徵 (Face Representation)
 人臉識別 (Face Identification)
 人臉驗證 (Face Verification /
Person Re-identification)
 表情分析 (Expression Analysis)
 生理分類 (Physical Classification)
人臉檢測
人臉識別方法對比
人臉識別方法對比 (續)
核心:卷積 (Convolution) 運算
特稱圖(feature map)
輸入(此處為5x5)
此處卷積核(convolution filter)大小為3x3
基本原則
局部連接(locally connected)
[區域感受野]
權重共享(weight sharing)
[共用學習參數]
每個卷積核逐一掃描輸入計算,各個卷積核擷取不同的
特徵。
(經過學習的) 卷積核
(學習到的) 階層特徵
深度神經網路階層架構
卷積層(convolutional layer)
其它人臉相關技術
估計數量 (Count / Estimate)
生成的擬真人臉
深偽 (Deepfake) 造假
*Video demo https://youtu.be/KfGB7VlvVI4
結合虛擬實境
*Video demo https://youtu.be/uflTK8c4w0c

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基於深度學習的人臉辨識技術簡介