狗鼻偵測與基於鼻紋的寵物狗身份辨識

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姓名

陸威穎(Lu Wei-Ying) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

狗鼻偵測與基於鼻紋的寵物狗身份辨識
(Dog′s Nose Segmentation and Nose-Print Pet Identity Recognition)

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至系統瀏覽論文 (2025-6-8以後開放)

摘要(中)

現代人飼養寵物的情況與日俱增，卻也因此出現更多如走失、棄養等問題，造成社會的負擔。現存的身分識別技術大多採RFID晶片植入，然而植晶的行為除了對寵物產生健康上的疑慮導致飼主意願降低以外，還必須搭配儀器的掃描，並且可能面臨植入晶片的失效，以上種種皆會造成實施上的困難。本研究中提出一套基於影像處理的寵物犬身份辨識系統，透過犬隻的鼻紋紋理特徵進行身份識別。影像中的狗鼻區域藉由U-Net深度神經網路進行偵測及分割，分割出的狗鼻區域影像結合了LBP、GLCM、GGCM等三種紋理分析演算法進行特徵擷取，並將取得的特徵以PNN進行身份辨識。我們改善了U-Net的batch normalization策略，大幅提升了鼻紋區域的影像分割性能，結合後續的決策融合PNN分類器，使得身份辨識正確性達到優異的表現。在我們自建的10隻寵物狗資料庫，我們的方法可獲得98%辨識率。本研究規劃了一個完整的寵物身份辨識流程，並使用U-Net做為影像分割的基礎，在少量學習樣本的情況下，仍具有良好的切割性能，提供正確且完整的狗鼻區域進行身份辨識。

摘要(英)

Nowadays, more and more people keep pets, as a result, more problems have happened, such as loss and abandonment, etc.…, causing a burden on society as well. Most of the existing identity recognition technologies are implanting the RFID chips. However, by this way, it will not only have the health concerns about pets, but also reduce the willingness of the owners to do so; besides, the chips implanted must be scanned by the instrument, and may face the failure of the implanted chips. The above reasons cause the difficulties in implementation of identity recognition.
In this study, a pet dog identity recognition system based on image processing is proposed, which uses dog nose texture features for identity recognition. The dog-nose area in the image is detected and segmented by the U-Net deep neural network. The segmented dog-nose area image combines three texture analysis algorithms such as LBP, GLCM, and GGCM for feature extraction, and the obtained Features are identified by PNN. We improved U-Net′s batch normalization strategy, which greatly improved the image segmentation performance of the nose pattern area, combined with the subsequent decision fusion PNN classifier, so that the accuracy of identity recognition reached excellent performance. In our self-built 10 pet dog database, our method can obtain 98% recognition rate. This study planned a complete pet identity recognition process, and used U-Net as the basis for image segmentation. With a small number of learning samples, it still has good cutting performance and provides correct and complete dog nose area for identity recognition.

關鍵字(中)

★ 狗鼻紋偵測
★ 狗鼻紋辨識
★ 生物辨識
★ 決策融合

關鍵字(英)

★ U-Net
★ LBP
★ GLCM
★ GGCM
★ PNN

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章、緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 論文架構 3
第二章、方法回顧 4
2.1 U-Net狗鼻切割 4
2.1.1 Up-Sampling 6
2.1.2 Skip-Connection 8
2.1.3 Overlap-Tile Strategy 9
2.2 鼻紋紋理特徵擷取 10
2.2.1 Local Binary Pattern 11
2.2.2 Gray Level Co-occurrence Matrix 14
2.2.3 Gray Gradient Co-occurrence Matrix 17
2.3 鼻紋分類器 19
2.3.1 機率神經網路 20
2.3.2 多模態機率神經網路融合分類器 21
第三章、系統架構 22
3.1 鼻紋身分辨識系統架構 22
3.2 U-Net狗鼻分割 24
3.3 狗鼻紋理特徵擷取 26
3.3.1 LBP特徵擷取模組 27
3.3.2 GLCM特徵擷取模組 28
3.3.3 GGCM特徵擷取模組 29
3.4 狗鼻身份分類器 30
3.4.1 PNN紋理分類模組 30
3.4.2 PNN決策融合模組 32
3.5 系統程式合成 33
第四章、系統整合與實驗 34
4.1 實驗平台 34
4.2 資料庫建置程序 35
4.3 影像分割模型建立 36
4.4 狗鼻分割實驗 40
4.5 狗鼻身份辨識實驗 46
第五章、結論與未來展望 51
5.1 結論 51
5.2 未來展望 52
參考文獻 53

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指導教授

陳慶瀚(Pierre Chen)

審核日期

2020-6-29

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