Abstract: Local Binary Pattern（LBP） encodes images by comparing 8 neighborhoods pixels to the central point which ignore the magnitude of neighborhoods, Local Intensity Order Pattern（LIOP） encodes neighbor pixels’ local ordinal information which works only in small numbers of neighbor pixels, aiming at these disadvantages, a new feature extraction algorithm with large number of neighbor pixels’ local ordinal information is proposed. The algorithm firstly gets the feature vectors which contain neighborhoods ordinal information, then uses the unsupervised clustering method（[K]-means） to reduce pattern numbers of feature vectors. This clustering process can be done offline and run efficiently. Finally, cascaded region histogram features of images are used as the final face representation. Experimental results show that ELQP descriptor achieves the best robustness and recognition rate among these compared descriptors. Furthermore, Whitened Principal Component Analysis（WPCA） is applied to get less feature dimensions and better recognition rate.

Key words: Local Binary Pattern（LBP）, Local Intensity Order Pattern（LIOP）, [K]-means, Whitened Principal Component Analysis（WPCA）

摘要： 针对局部二值模式没有考虑邻域点之间的关系以及局部序数模式（LIOP）的邻域点数过少不足，提出一种利用大邻域范围内邻域点间序数信息的特征提取算法。该算法首先以类似LIOP编码的方式得到的邻域特征向量，然后应用[k]均值聚类算法降低特征向量的主模数量。同时此聚类过程可以离线进行并且运行十分高效；最终将级联直方图特征作为人脸特征向量。实验结果表明，该方法的鲁棒性和识别率均优于对比算法。最后应用WPCA算法既降低特征维数又提升了算法的识别率。

关键词: 局部二值模式, 局部序数模式, [K]均值, 白化主成分分析