Local Binary Pattern (LBP) represents a circular derivative pattern generated by the concatenation of the binary gradient directions. However, the pattern fails to extract more detailed information such as texture feature contained in the input object. In this thesis, we propose a texture classification based on median vectors, circular derivative patterns, and directional patterns for face recognition. The problem of pixel classified into the inappropriate bin in LBP is solved completely by including the modified SQMV feature. With the texture feature, we could apply this method only on local regions rather than whole face image to reduce the computation complexity and speed up the execution time. The experiment result shows the face recognition rate of our approach is better than the original LBP method. The average execution time ratio of our proposed methods to the original LBP method and the E-GV-LBP method are only about 24% and 1.11% respectively. The robustness against four types of noise model (Gaussian noise, impulse noise, uniform impulse noise and mixed Gaussian and impulse noise) are also demonstrated.

傳統的局部二元圖樣方法以一連串的二元梯度方向來表示一個圓形導數圖樣。然而局部二元圖樣方法並不能獲取更多細部的資訊，例如輸入影像的紋理特徵。本篇論文中，我們針對人臉識別提出了一個基於中間值向量、圓形導數圖樣與方向圖樣的紋理分類法。在局部二元圖樣方法中，像素可能被分類到不適當柱(bin)的問題。透過我們所提出的良式象限中間值排序向量，上述的問題已經完善的解決。藉由所提出的紋理特徵，我們將這個方法應用到局部區域來取代全臉區域來簡化計算複雜度並加速執行時間。在人臉辨識率上，實驗結果顯示我們所提出方法比傳統的局部二元圖樣方法更好；而在平均執行時間上，實驗結果顯示我們所提出的方法對於局部二元圖樣方法與基於有效賈伯體(Gabor volume)之局部二元圖樣方法的平均執行時間比分別為24%與1.11%。針對高斯雜訊、脈衝雜訊、均勻脈衝雜訊、混合高斯與脈衝雜訊等四種不同雜訊模型的強健性在實驗中也得到了驗證。

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