臺灣博碩士論文加值系統

在犯罪日益猖獗的今日，無論是住家、企業甚至是國家，均利用各式科技來管制進出者的身份或是保護個人資料。人類身上有許多可用於辨識的特徵，這些特徵具有獨特性、辨別性、永久不變性、可量化性等特性。而這些特徵可分為生理特徵（如：指紋、臉形、虹膜等）與行為特徵（如：簽名、打字習慣、步態等）。其中又以步態可不受環境所限制，可將偵測設備設置於與被監測者相隔之一段距離外，亦不需要被監測者的配合即可容易獲取步態參數。目前關於步態辨識的文獻主要分為三個部份：（1）影像辨識，藉由影像處理的方式將步態特徵參數擷取並將其應用於辨識系統中；（2）藉由心理學的角度，觀察人是如何判別他人；（3）以步態之生物力學參數觀察識別結果。此類的文獻卻鮮少提到或整理辨識參數之重要性，故本研究以心理學角度出發，探討觀測者使用辨識參數之情形，並藉由下肢生物力學參數為例觀察不同面向、單一肢段參數做為識別參數與不同行走條件其個別識別之影響。以心理學的角度出發，探討觀測者用於識別之觀測參數，研究結果顯示，人類所使用之識別觀測參數會因給予不同攝影角度影片所影響，且依本研究排序分析之分區觀測參數重要性結果顯示，手部與腿部的部份皆為較重要的觀測區域。當給予觀測者不同角度影片時，所使用的觀測參數會有不同。據研究結果顯示，觀測者至少需5個觀測參數時，較容易成功判斷與識別，且走路速度、體型、走路頻率、步長、手部擺動幅度、下手臂擺動幅度等觀測參數則為較多攝影角度中之重要觀測參數。而辨識率與辨識時間皆不受觀測者角度所影響。本研究之實驗設計包含識別同人與不同人的測試，由結果推測識別同人與不同人亦可能使用不同之觀測參數，但因本研究主要探討觀測參數的重要性為主，故未探討人類識別同人與不同人時觀測參數之差異。若影像識別研發參考人類識別之觀測參數，則識別同人與不同人之方法亦為另一重要課題。因為減少識別參數、減少電腦運算量，且避免手部有其他非一般行走時之動作，故本研究之生物力學參數僅是以下半身參數做為識別範例，結果發現利用K最近鄰域分類法（K-Nearest Neighbor Classification, KNN）此演算法不僅可解決不同狀態下的步態差異（一般行走與跑步機上行走差異），且研究結果顯示慣用腳的大小腿的辨識能力較佳，因此若需要較少的資訊做為識別參數，慣用腳的大小腿資訊則為較佳的辨識參數選擇。

Nowadays, individual identification is gaining more and more importance. Face recognition and finger print recognition are the most popular methods of identification. However, some limitations of these methods exist. Distance between detectors and individual, for instance, cannot be too far. Walking acquaintances conquer these limitations. People can be identified by walking acquaintances even when they are too far to be recognized by their faces. Gait recognition, one of the individual identification methods, has potential to be applied to forensic use. In the computer science field, most of the studies used the shape of silhouette on subject’s sagittal plane for data classification and calculated the dynamic parameters such as angular motion of limb from the silhouette, but few studies concerned about which parameter is important or about which camera view can gain better recognition rate. In the psychology field, few studies asked the viewers to give some responds about which parameters were used for recognizing the target subject in the video. Although the recognizing parameters were obtained from the viewers, the level of importance for each parameter is not clear. In the biomechanical field, kinetics and kinematics data of lower limbs were used for individual identification. Few studies address on the important of recognition parameters for identification. The objectives of this study are: (1) to determine how many details in segmental area or kinematics need for individual identification and (2) to determine which segment or which plane were significance for identifying individuals by taking the biomechanical parameters of lower limb as an example. Forty-one viewers in the human recognition test were asked to take a questionnaire after they recognized the subjects in the video. These questionnaires would be analyzed with accuracy. Based on the results, getting an increasing rate had to select five options at least. Rank of each parameter was obtained by Ψ correlation of parameter selection and answer results, ρ correlation of accuracy and parameter-selecting rate. Most important parameters were walking speed, body size, cadence, step length, range of arm swing and range of lower arm swing. According to the different shot angles, viewers would adopt different parameters to identify the subjects. The accuracy and time spent would not be affected by shot angles. Last but not least, the study established bionic recognition system with K-Nearest Neighbor Classification (KNN), and demonstrated it by adopting biomechanical parameters of lower limbs. KNN not only solved the difference between overgound and walking on the treadmill, but also found the importance of the parameters of frontal plane. Dominant leg was gain better recognition rate than another. For developing biometrics, those parameters should be concerned for individual identification.

摘 要 i
ABSTRACT iv
誌 謝 vi
目 錄 vii
表目錄 ix
圖目錄 x
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 7
二、 文獻回顧 8
2.1 影像步態辨識 8
2.1.1 Model-free methods 9
2.1.2 Model-based methods 10
2.2 心理學相關文獻 19
2.3 生物力學參數步態辨識 24
2.4 小結 29
三、 步態特徵參數擷取與探討 31
3.1 人類步態識別參數 31
3.2.1 人類步態識別參數實驗結果與討論 39
3.2.2 人類步態識別參數研究限制 70
3.2 生物力學辨識參數 71
3.1.1 辨識演算法 72
3.1.2 生物力學參數實驗結果與討論 81
3.1.3 生物力學參數實驗研究限制 90
四、 結論 91
五、 未來方向 93
六、 參考文獻 94
附錄A 101
附錄B 104

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