多模態生物特徵神經網路分類器應用於寵物身分識別

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陳昌暐(Chang-Wei Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

多模態生物特徵神經網路分類器應用於寵物身分識別
(Multi-Modal Biometric Neural Network Classifier for Pet Identity Authentication)

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摘要(中)

目前政府對於寵物和流浪動物管理主要採用的方法是植入RFID晶片，用以識別寵物身分。由於採用侵入式手段，民眾為寵物植入晶片意願不高，導致管理漏洞。本研究針對以非侵入的影像辨識方法進行寵物身分識別，提出一個多模態生物辨識方法。我們先藉由CNN分類出犬種，再擷取寵物鼻紋、身形輪廓及臉部幾何等多模態生物特徵，最後藉由混合神經網路分類器進行犬隻身分識別。最後我們設計了一個多模態混合神經網路（MM-HNN）分類器系統來驗證寵物辨識的性能。實驗比較顯示，單純使用CNN深度學習模型從事寵物身分識別的等錯誤率為23.77%，MM-HNN寵物身分識別的等錯誤率為13.45%，而CNN犬種辨識加上MM-HNN身分識別的等錯誤率為4.65%；在三群同胞犬的實驗中，身分識別的等錯誤率為4.57%；在三種生物特徵模態的辨識實驗中，在紋理模態中辨識率為88.33%，輪廓模態中辨識率為84.83%，臉部模態中辨識率為79.83%，最後混合三種模態而成的多模態辨識率則達到95%，因此證明了多模態混合神經網路分類器具有良好的辨識性能。

摘要(英)

At present, the government mainly uses embedded RFID chip to supervise pets and stray animals to identify the pets. As the means is invasive, common people′ willingness to embed the chip in pets is not high, leading to administrative vulnerability. This study proposes a multi modal biological recognition method for using non-invasive image recognition method to identify pets. The dog species is classified by CNN (Convolutional Neural Network), and then the pet′s multi modal biological features are extracted, such as muzzle pattern, body contour and facial geometry. Finally, the dog is identified by hybrid neural network classifier. A Multi Modal Hybrid Neural Network (MM-HNN) classifier system is designed to validate the performance of pet identification. The empirical comparison shows that the Equal Error Rate (EER) of simply using CNN deep learning model for pet identification is 23.77%, the EER of MM-HNN pet identification is 13.45%, and the EER of CNN dog species recognition + MM-HNN identification is 4.65%. In the experiment on three groups of fellow dog, the EER of identification is 4.57%. In the recognition experiment on three biological feature modals, the texture modal recognition rate is 88.33%, the contour modal recognition rate is 84.83%, the face mode recognition rate is 79.83%, and the multi modal recognition rate of the three modals is 95%, proving that the MM-HNN classifier has good recognition performance.

關鍵字(中)

★ 多模態

關鍵字(英)

論文目次

摘　要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章、緒論 1
1.1 研究背景 1
1.2 研究目標 2
1.3 論文架構 3
第二章、文獻回顧 4
2.1 紋理特徵抽取 4
2.1.1 灰階共生矩陣 4
2.1.2 灰階梯度共生矩陣 8
2.1.3 局部二值型態 10
2.1.4 Tamura紋理特徵 12
2.2 輪廓特徵抽取 15
2.3 類神經網路 17
2.3.1 機率神經網路 17
2.3.2 自組織映射圖網路 18
2.3.3 多層前饋神經網路 21
2.4 卷積神經網路 24
第三章、多模態類神經網路分類器 30
3.1 架構概述 30
3.2 CNN犬種辨識 31
3.3 多模態混合神經網路分類器 32
3.4 多模態寵物身分識別 33
第四章、多模態寵物辨識系統架構設計 39
4.1 MIAT設計方法論 39
4.1.1 IDEF0階層式模組化設計 40
4.1.2 Grafcet 41
4.2 系統架構 43
4.3 離散事件建模 47
4.3.1 影像前處理 48
4.3.2 抽取特徵 49
4.3.3 抽取輪廓特徵 50
4.3.4 抽取臉部特徵 51
4.3.5 抽取紋理特徵 52
4.3.6 類神經網路分類器 54
第五章、實驗 55
5.1 實驗環境 55
5.1.1 犬種影像資料庫 55
5.1.2 自建實驗資料庫 57
5.2 辨識性能評估實驗 58
5.3 寵物身份識別實驗 60
5.4 同胞犬實驗 64
5.5 訓練樣本數實驗 69
5.6 三種生物特徵模態的辨識實驗 72
第六章、結論 76
6.1 結論 76
6.2 未來展望 77
參考文獻 78

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指導教授

陳慶瀚

審核日期

2018-7-2

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