指紋具有個化能力，因此指紋鑑定是偵查人員偵辦刑案時的利器之一，也是院檢起訴、審判時常常引用的重要證據，而我國指紋比對須滿足12個特徵點相符才能判定兩枚指紋相符，惟刑案現場遺留之潛伏指紋（latent fingerprints）相較於標準指紋卡上的三面印（rolled）、平面印（plain）指紋，通常有紋線不清的問題，鑑定人員若無法從中找出12個以上的特徵點，便會認定該現場指紋的特徵點不足，無法進行後續比對，因此，若能針對紋線不清的區域進行強化，使更多的指紋特徵點得以辨識，讓後續比對階段可以順利進行，除了可以增加比中率，亦提升鑑定人員的比對效率，對指紋鑑定工作將有相當大的幫助。
本研究以pix2pix網路為基礎，建構強化潛伏指紋紋線的條件式生成對抗網路（FEGAN），並使用IIIT-D latent fingerprint、IIIT-D MSLFD資料集的真實潛伏指紋影像作為訓練樣本。實驗結果使用IIIT-D MOLF DB4 Latent資料集的NFIQ2分數進行評估，結果顯示，NFIQ2分數0以上的樣本比例，可由強化前的2.8%提升至36.8%，以FpMV指紋特徵點檢視工具觀察，強化後的特徵點數明顯增加，指紋辨識準確率方面，與DB1 Lumidgm比對結果，Rank-50準確率可由強化前的6.06%，大幅提升至46.73%。

Latent fingerprints left at crime scenes are usually unclear. Therefore, if the areas with unclear ridges can be enhanced, more minutiae can be identified, enabling the following comparison stage to be carried out smoothly. Based on the pix2pix, this study constructs a fingerprint enhancement conditional generative adversarial network (FEGAN) that enhances the ridges of latent fingerprints and uses the authentic latent fingerprint images of the IIIT-D latent fingerprint and IIIT-D MSLFD datasets as training data. The experimental results are evaluated by using the IIIT-D MOLF DB4 Latent dataset. The results show that the proportion of samples with NFIQ2 score above 0 was increased from 2.8% to 36.8%. Observed with the FpMV, the number of minutiae after enhancement increased significantly. In terms of fingerprint identification accuracy, the Rank-50 accuracy was greatly improved from 6.06% to 46.73% when compared with the DB1 Lumidgm. These results show that FEGAN was able to distinguish between fingerprints and interfering factors after the training, resulting in clearer ridges than before the enhancement.

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