運用模糊邏輯與類神經網路的指紋識別系統

對自動指紋辨識系統而言，正確的特徵擷取是非常重要的。然而，品質不良影像中的雜訊常會造成特徵擷取錯誤，像是無法正確找出特徵點或是誤判特徵點。為了改善這些現象，目前有很多建立在精確數學模式之上的指紋辨識系統，嘗試解決此一問題，但是都無法適當地處理錯誤的現象。我們都知道，人們對於指紋圖案有極佳的辨識能力，因此，本篇論文運用類似人類思維的方式，應用模糊邏輯與類神經網路，成功地結合模糊理論具有容錯及倒傳遞類神經網路回想速度快之特性，實做具有容錯性且快速的指紋分析比對資料庫系統。每筆指紋資料經模糊化後，再輸入倒傳遞類神經網路訓練後建檔，所耗費的時間約為3秒。指紋資料庫比對每筆樣本平均需時0.08秒。系統在相似度閥值為0.9之情形下，其拒真率為0％，平均讓假率約為0.23％。由以上數據可得知此方法是強健、可靠且快速的。

關鍵字

指紋辨識；模糊編碼器；倒傳遞類神經網路

並列摘要

The correct minutiae extraction is very important in an automatic fingerprint identification system. However, the presence of noise in poor-quality images will cause many extraction faults, such as the dropping of true minutiae and inclusion of false minutiae. Nowadays, most fingerprint identification systems are based on precise mathematical models, but they can not handle such faults properly. As we know, human beings are good at recognizing fingerprint pattern. Therefore, a human-like method is applied. This paper presents an adaptive fuzzy logic and neural network method which is fast and has variable fault tolerance. We implement a fast fingerprint database system with fault tolerance. Before neural network training, every fingerprint is encoded by a fuzzy image encoder. Then the result of training is saved in a database. The training time is 3 seconds. The matching time is 0.08 second. When the threshold is 0.9, the FAR is 0% and FRR is 0.23%. Our experimental results have shown that this fingerprint identification method is robust, reliable and rapid.

並列關鍵字

Fingerprint Identification ； Fuzzy Encoder ； BPNN

參考文獻

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運用模糊邏輯與類神經網路的指紋識別系統

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