指紋已經廣泛的應用在身份辨識上。然而，從犯罪現場所取得指紋資料有時遇到兩枚以上的重疊指紋，但是重疊指紋分離成兩枚清晰的單枚指紋是目前研究的課題。在本文中，提出了一種新的演算法，將指紋先分類為單枚與兩枚重疊指紋，再分離為單枚指紋。首先，利用HOG進行特徵萃取，將特徵排列成矩陣後，進行PSD運算訓練模板，將指紋分類。再利用橢圓遮罩計算單枚指紋的覆蓋面積，定義單枚指紋適當的長短軸的初始遮罩。接著，使用初始遮罩掃描重疊指紋計算覆蓋面積，計算所有遮罩之覆蓋面積。再接著，同時取兩個單枚橢圓遮罩與原圖計算上述遮罩方向與位置覆蓋面積，找出重疊面積最大者，即可從兩枚重疊指紋分離成兩枚部分清晰與部分不清晰的指紋。 Fingerprint has been used as a tool in human identification widely. But, fingerprint latent obtained occasionally from the crime scene is overlapping by two or more fingerprints. Therefore, how to separate overlapped latent into clear single fingerprint is a hot research issue. In this paper, we propose a new algorithm to separate overlapping fingerprints into single fingerprints. First use HOG to extract the feature and arrange in matrix. Perform a PSD training train template to classify the fingerprints. Next, to find the size of initial mask for fitted single fingerprint, we use elliptical mask to define the size of semi-major and semi-minor axes via statistical analysis The coverage area is calculated by using two single oval masks and the original figure. Finally, find the largest overlapping area. We can split two overlapping fingerprints into two parts clear and partially unclear fingerprints.
