指紋影像評估主要應用於指紋感測器的影像品質評估。一個設計良好的指紋影像評估系統同時有助於提升指紋註冊的正確性，並有效提升指紋辨識率。本研究提出了一個結合紋理特徵分析及機率神經網路(Probability Neural Network，PNN)的指紋影像評估系統，以便改良現有的指紋影像品質評估方法。本研究的指紋影像評估流程可分為紋理特徵分析，PNN神經網路分類，整合PNN分類結果與傳統NFIQ評估，得到最終品質分數。就實驗結果顯示，結合紋理特徵分析的指紋影像評估可以提升評估指紋品質時的精確度。 

Fingerprint image assessment is primarily applied to assess the quality of images stored in fingerprint readers. A satisfactorily designed fingerprint image assessment system effectively improves the accuracy of fingerprint registration and the rate of fingerprint recognition. This study incorporated a new fingerprint image assessment system that combines fingerprint texture analysis with a probability neural network (PNN) to improve the existing fingerprint image quality assessment method. The fingerprint image assessment procedure involved texture analysis and PNN neural network classification. Subsequently, the PNN classification results were organized and compared to the conventional National Institute of Standards and Technology fingerprint image quality assessment results to obtain the final quality scores. According to the result of the experiment, the fingerprint image assessment system that incorporated texture analysis improved the accuracy of fingerprint image quality assessment.

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