指紋奇異點是指紋影像的重要特徵，其代表了指紋脊線與谷線的紋理方向特性，因此偵測奇異點位置與數量對於指紋辨識相當重要。傳統的奇異點偵測方法：龐加萊指標易受到局部方向性雜訊影響，因此找到過多參考點，或者漏失真實的參考點。本文提出了一個改善傳統方法的奇異點偵測方法，透過離散小波轉換影像前處理，降低影像雜訊，接著應用蓋柏濾波器強化指紋紋路型態，提高了指紋影像的影像品質，再以龐加萊指標偵測可能的指紋奇異點，以每個可能的奇異點為中心，計算其周圍區域的(LBP)紋理特徵向量，以此篩選參考點，降低誤判率。實驗結果顯示，相對於傳統方法，本方法能夠降低因方向場雜訊而找到的假奇異點的機會，提高指紋奇異點偵測率。;Singularity points are key features in fingerprint images, representing the flow patterns of ridges and valleys. Therefore, the position and quantity of singularities are crucial in fingerprint recognition systems. In conventional singularity detection methods, the Poincaré index is prone to influence by local directional noises. This often results in excessive false reference points or the loss of true reference points. This study proposed an improved singularity detection method through applying a series of processing techniques comprising discrete wavelet transform image preprocessing for reducing image noise, Gabor filtering for strengthening the fingerprint patterns and enhancing fingerprint images, and Poincaré indexing for detecting possible singularities. In the detection process, local binary patterns surrounding every possible singularity were calculated to filter false reference points and reduce error judgments. The experimental results show that, in contrast to conventional methods, this method effectively reduced the number of false singularities caused by directional noise, thereby enhancing the detection rate of the fingerprint singularities.