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    Title: 運用臉部追蹤系統於頭部動作偵測
    Other Titles: Using the face tracking technology to detect head action
    Authors: 柯政宇;Ko, Chen-Yu
    Contributors: 淡江大學資訊工程學系碩士班
    陳瑞發;Chen, Jui-Fa
    Keywords: 臉部偵測;頭部偵測;主動形狀模型;指數加權移動平均;編輯距離;face tracking;Head Tracking;Active Shape Model;EWMA;Levenshtein Distance
    Date: 2013
    Issue Date: 2014-01-23 14:32:33 (UTC+8)
    Abstract: 目前電腦的輸入設備的操作方式大多是藉由雙手來做操作,但是這種操作方式對於殘障人士,特別是肢體殘障人士相當的不方便,目前常見的電腦輔助操作有兩種,眼睛追蹤和頭部追蹤,眼睛追蹤可以表達的動作較少,而頭部追蹤有超音波、紅外線、臉部追蹤三種方式,前兩者需要配戴器具,而臉部追蹤則不需要,對於殘障人士來說使用上較為舒適。
    本論文藉由主動形狀模型追蹤使用者臉部的特徵點,計算出頭部的位移與轉動;並利用指數加權移動平均來平滑因為使用者不自覺的頭部晃動而造成的位移與轉動的資料偏差,再結合編輯距離演算法與頭部的位移資料,判斷使用者頭部做出的位移軌跡,判斷使用者作出了哪個事先定義的動作;最後我們將這些資訊對應至相應的電腦操作,提供殘障人士一個簡易的操作電腦方法。
    Current computer input device’s control method is mostly done by hand operations, but this mode of operation for people with disabilities, especially physical disabilities is quite inconvenient. There are two common computer accessibility for people with disabilities: eye-tracking and head tracking. Eye tracking express less action, and head tracking has three implement method: ultrasound, infrared and face tracking. The implementation use ultrasound or infrared both need to wear equipment, but face tracking is not required, to use for people with disabilities, it is more comfortable.
    In this thesis, we use Active Shape Model (ASM) to track user’s face feature point, and calculate the translate data and rotation data of the Head, then we use Exponentially-Weighted Moving Average (EWMA) filter to smooth the ''jittery'' raw-data which produce by user because of unconsciously head shaking, then use Levenshtein Distance algorithm and translation data to detect which defined action that user did, at last we match the head action data to the corresponding operation, providing people with disabilities a simple method to control computer.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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