Opencv 基于LBP的人脸表情精确检测算法的改进

我正在开发一个简单的算法来检测几种面部表情（快乐、悲伤、愤怒……）。我有理由这么做。在应用LBP均匀算子之前，我正在进行预处理，将标准化图像划分为6x6区域，如下例所示：

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通过应用统一的LBP，每个区域提取了59个专长，因此最终我得到了2124个图像专长（6x6x59）。我认为当我有大约700张图片来训练一个模特时，这是一个太多的壮举了。我读到过，要得到一个好的精度是不好的。我的问题是如何降低专长或其他技术的维度以提高算法的精度。

降低特征维度的一种简单方法-同时提高鲁棒性-将使用。对于半径为圆形且由像素构成的邻域，纹理描述符通过10个特征表示每个区域。因此，维数从2124降到了6×6×10=360。

PCA可以帮助减少描述符的大小，而不会丢失重要信息。只需谷歌“opencv pca示例”

另一个有用的方法是为均匀lbp特征添加旋转不变性。这将提高精度，并将描述符的大小从59大幅减少到10

static cv::Mat rotate_table = (cv::Mat_<uchar>(1, 256) <<
                               0, 1, 1, 3, 1, 5, 3, 7, 1, 9, 5, 11, 3, 13, 7, 15, 1, 17, 9, 19, 5, 21, 11, 23,
                               3, 25, 13, 27, 7, 29, 15, 31, 1, 33, 17, 35, 9, 37, 19, 39, 5, 41, 21, 43, 11,
                               45, 23, 47, 3, 49, 25, 51, 13, 53, 27, 55, 7, 57, 29, 59, 15, 61, 31, 63, 1,
                               65, 33, 67, 17, 69, 35, 71, 9, 73, 37, 75, 19, 77, 39, 79, 5, 81, 41, 83, 21,
                               85, 43, 87, 11, 89, 45, 91, 23, 93, 47, 95, 3, 97, 49, 99, 25, 101, 51, 103,
                               13, 105, 53, 107, 27, 109, 55, 111, 7, 113, 57, 115, 29, 117, 59, 119, 15, 121,
                               61, 123, 31, 125, 63, 127, 1, 3,  65, 7, 33, 97, 67, 15, 17, 49, 69, 113, 35,
                               99, 71, 31, 9, 25, 73, 57, 37, 101, 75, 121, 19, 51, 77, 115,  39, 103, 79, 63,
                               5, 13, 81, 29, 41, 105, 83, 61, 21, 53, 85, 117, 43, 107, 87, 125, 11, 27, 89,
                               59, 45, 109, 91, 123, 23, 55, 93, 119, 47, 111, 95, 127, 3, 7, 97, 15, 49, 113,
                               99, 31, 25, 57, 101, 121, 51, 115, 103, 63, 13, 29, 105, 61, 53, 117, 107, 125,
                               27,  59, 109, 123, 55, 119, 111, 127, 7, 15, 113, 31, 57, 121, 115, 63, 29, 61,
                               117, 125, 59, 123, 119, 127, 15, 31, 121, 63, 61, 125, 123, 127, 31, 63, 125,
                               127, 63, 127, 127, 255
                               );

// the well known original uniform2 pattern
static cv::Mat uniform_table = (cv::Mat_<uchar>(1, 256) <<
                          0,1,2,3,4,58,5,6,7,58,58,58,8,58,9,10,11,58,58,58,58,58,58,58,12,58,58,58,13,58,
                          14,15,16,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,17,58,58,58,58,58,58,58,18,
                          58,58,58,19,58,20,21,22,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,
                          58,58,58,58,58,58,58,58,58,58,58,58,23,58,58,58,58,58,58,58,58,58,58,58,58,58,
                          58,58,24,58,58,58,58,58,58,58,25,58,58,58,26,58,27,28,29,30,58,31,58,58,58,32,58,
                          58,58,58,58,58,58,33,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,34,58,58,58,58,
                          58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,
                          58,35,36,37,58,38,58,58,58,39,58,58,58,58,58,58,58,40,58,58,58,58,58,58,58,58,58,
                          58,58,58,58,58,58,41,42,43,58,44,58,58,58,45,58,58,58,58,58,58,58,46,47,48,58,49,
                          58,58,58,50,51,52,58,53,54,55,56,57
                          );

static cv::Mat rotuni_table = (cv::Mat_<uchar>(1, 256) <<
                         0, 1, 1, 2, 1, 9, 2, 3, 1, 9, 9, 9, 2, 9, 3, 4, 1, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9,
                         3, 9, 4, 5, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9,
                         3, 9, 9, 9, 4, 9, 5, 6, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
                         9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
                         3, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 5, 9, 6, 7, 1, 2, 9, 3, 9, 9, 9, 4, 9, 9, 9, 9,
                         9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 9,
                         9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 7, 2, 3, 9, 4,
                         9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 7,
                         3, 4, 9, 5, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 7, 4, 5, 9, 6, 9, 9, 9, 7, 5, 6, 9, 7,
                         6, 7, 7, 8
                         );

static void hist_patch_uniform(const Mat_<uchar> &fI, Mat &histo,
                               int histSize, bool norm, bool rotinv)
{
        cv::Mat ufI, h, n;
        if (rotinv) {
                cv::Mat r8;
                // rotation invariant transform
                cv::LUT(fI, rotate_table, r8);
                // uniformity for rotation invariant
                cv::LUT(r8, rotuni_table, ufI);
                // histSize is max 10 bins
        } else {
                cv::LUT(fI, uniform_table, ufI);
        }
        // the upper boundary is exclusive
        float range[] = {0, (float)histSize};
        const float *histRange = {range};
        cv::calcHist(&ufI, 1, 0, Mat(), h, 1, &histSize, &histRange, true, false);

        if (norm)
                normalize(h, n);
        else
                n = h;
        histo.push_back(n.reshape(1, 1));
}

static cv:：Mat rotate_table=（cv:：Mat_）（1256）