第五章、 結論與未來展望
5.2. 未來展望
本論文目前提出之辨識系統，藉由時序分析，能夠對情緒做更細緻之描述與 辨識，但其仍有幾點問題必須解決。
特徵點擷取方面，本研究所使用之 AAM 演算法，對於人臉情緒形變過大之 表情無法準確擷取人臉特徵，主要原因在於其與樣板影像差別過大所致，或許可 藉由建立更豐的資料庫，或加入局部特徵做改善。另外，就運算速度而言，AAM 建立人臉模型之速度仍有加強之空間，若前述之 AAM 準確性有所改善，可透過 影像追蹤之方式，每次計算形變模型與紋理模型時，以前一次之人臉模型為基礎，
迭代計算此次之人臉模型參數，降低迭代次數與運算量。
情緒辨識方面，本研究雖然加入時序對情緒做分析，但其仍以靜態情緒辨識 為主，對話情境之動態情緒辨識則未能達到其辨識效能，主因仍在於運算速度與 時序分析之完善性。在系統運算量納入考量情況下，透過更深入之時序分析，與 人臉變化頻率分析，設法擷取人臉表情關鍵之辨識特徵，可以做為未來之探討方 向，以求更自然、更廣範之人機互動為目標。
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附錄一、基本人臉情緒混合比例問卷調查樣張
受測者基本資料：
性別: 女 年齡:28
問卷說明：
請依序填入各影像所含基本情緒 Neutral(中性, Ne), Anger(生氣, An), Disgust(憎 惡、厭惡, Di), Fear(害怕, Fe), Happy(高興, Ha), Sadness(傷心, Sa), Surprise(驚訝, Su)之百分比，百分比加總必須為 100%。
例如:
中性: 20 生氣: 40 厭惡: 10 害怕: 15 高興: 0 傷心: 15 驚訝: 0
S002 S026
指出 S002 影像所包含基本情緒比例之百分比 指出 S026 影像所包含基本情緒比例之百分比
中性: 0 生氣:70 厭惡:30 害怕: 0 中性: 40 生氣: 30 厭惡: 30 害怕:0
高興: 0 傷心: 0 驚訝:0 高興: 0 傷心: 0 驚訝: 0
101
S074 S015
指出 S074 影像所包含基本情緒比例之百分比 指出 S015 影像所包含基本情緒比例之百分比
中性: 90 生氣: 0 厭惡: 0 害怕: 0 中性: 70 生氣: 0 厭惡:0 害怕: 0
高興: 0 傷心: 0 驚訝: 10 高興: 30 傷心: 0 驚訝: 0
S013 S028
指出 S013 影像所包含基本情緒比例之百分比 指出 S028 影像所包含基本情緒比例之百分比
中性: 20 生氣: 0 厭惡: 60 害怕: 0 中性: 30 生氣: 0 厭惡: 0 害怕: 10
高興: 0 傷心: 0 驚訝: 20 高興: 0 傷心: 0 驚訝: 60
S073 S009
指出 S073 影像所包含基本情緒比例之百分比 指出 S009 影像所包含基本情緒比例之百分比
中性: 10 生氣: 0 厭惡:0 害怕: 20 中性: 10 生氣: 0 厭惡: 20 害怕: 0
高興: 0 傷心: 0 驚訝: 70 高興: 0 傷心: 70 驚訝: 0
102
S025 S004
指出 S025 影像所包含基本情緒比例之百分比 指出 S004 影像所包含基本情緒比例之百分比
中性: 80 生氣: 10 厭惡: 10 害怕: 0 中性: 0 生氣: 40 厭惡: 60 害怕: 0
高興: 0 傷心: 0 驚訝: 0 高興: 0 傷心: 0 驚訝: 0