對於人臉表情辨識系統，擷取影像特徵是造成整個系統好壞最重要的關鍵。
本論文提出一個方法，將表情影像以 SURF 進行特徵點計算，進而找出較具有辨 識效果的區域進行特徵擷取。擷取方法是透過 CS-LTP 與 LSDP 分別計算出區域內 各個像素點的特徵值後將其統計成特徵直方圖，再將兩特徵直方圖進行串接，形 成一個混合特徵直方圖，以此作為該張表情之特徵資訊；比起個別單獨使用 CS-LTP 或 LSDP 的方法，更能提升整體的辨識效能。
針對本論文所提之方法，主要研究結論有二：
1. 特定區域選取：由於人臉影像包含太多資訊，如何選出有利於辨識的區 域進行特徵擷取，是影響人臉表情辨識系統的關鍵。本論文所提之特定 區域選取方法，以具有高度穩健性及計算速度快等優點的 SURF 進行特 徵點計算，再根據這些特徵點選取出特徵擷取區塊。實驗結果顯示以此 方式所擷取出之區塊，具有一定程度的辨識能力，若搭配不同的特徵擷 取方法，都能使整體辨識效能得到提升。
2. 特徵擷取方法之結合：特徵擷取方法經過許多學者不斷創新、改良，已 產生出許多類型的擷取方式，每一種方式都有其優缺點。如何將某方法 之優勢突顯出來？或要以何種方式，進行不同方法的結合？本論文提供 了一種可行的方式，從實驗結果顯示，相較於單獨執行個別方法，確實 能得到更好的辨識效果。
因為特徵擷取之方法不斷推陳出新，未來可以嘗試以更好的擷取方法做結合，
或是以更好的演算法找出人臉影像的重要區域進行特徵擷取。為了使人臉表情辨 識系統更具有適用性，也可以更多表情資料庫進行方法的測試，亦可針對使用的 環境不同選擇特定表情資料庫進行開發，例如：憂鬱症患者表情資料庫、嬰幼兒
表情資料庫等。隨著科技進步，智慧型手持裝置越來越普及，若能將人臉表情辨 識系統結合至手持裝置當中，將可大幅提高其實用性。
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