在近期人臉辨識系統的相關研究中，注重人臉的表示式，並發掘多種方法去解析人臉在表情與光線變化影響下仍可取得的穩定特徵。其中一種便是區域二元圖樣，其特性包含運算簡單且有穩健的區域紋理特徵，本論文提出的區域紋理特徵便是經由動態線性決策函數，以延伸區域二元圖樣至以向量和以方向純量為基礎的區域紋理特徵，並應用於人臉辨識。首先是區域向量特徵，其提供新的向量表示式與編碼方式(比較空間轉換)，以產生更具有鑑別力的區域紋理特徵。接著是區域方向分類特徵，其利用額外的鄰邊向素以產生八個方向的邊之權重值，再取其極大值與極小值的方式進行編碼。以上所提出的方法皆經實作，並利用FERET，CAS-PEAL，CMU-PIE，Extend Yale B及LFW資料庫，比較區域二元圖樣和現今已有的區域紋理特徵方法。實驗結果證明本論文所提出來的方法，不論是在灰階影像或賈伯特徵，皆優於其他比較的方法。;Recently, the research in face recognition has been focused on developing a face representation that is designed to generate invariant features for solving facial illumination and expression. Motivated by a simple but powerful local pattern descriptor, Local Binary Pattern (LBP), two novel local pattern descriptors are proposed to extend the LBP to vector-based and directional-based local pattern descriptors via dynamic linear decision function for face recognition. The first descriptor, namely, Local Vector Pattern (LVP), provides a novel vector representation and a coding scheme Comparative Space Transform (CST), which are used to generate more detailed discriminative local features than the other methods. The second proposed descriptor, namely, Local Directional Classifier Pattern (LDCP), computes eight edge response values from extra neighborhood pixels, and these values are used to select the upper and lower bound indices for generating robust complete binary codes. These methods are implemented and compared with existing LBP face recognition systems and other state-of-art local pattern descriptors on FERET, CAS-PEAL, CMU-PIE, Extend Yale B, and LFW databases. Experimental results demonstrate that the proposed methods outperform the other comparative methods with grayscale images and Gabor features as inputs.
