使用OpenCV中的DNN模块进行人脸识别的官方示例代码（C++版本）：

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#include <opencv2/core.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

using namespace cv;
using namespace dnn;

int main()
{
    // 加载模型和配置文件
    String model_path = "path_to_model_file";
    String config_path = "path_to_config_file";
    Net net = readNetFromCaffe(config_path, model_path);

    // 读取输入图像
    Mat image = imread("path_to_input_image");

    // 构建blob，将图像输入模型中进行预处理
    Mat blob = blobFromImage(image, 1.0, Size(300, 300), Scalar(104.0, 177.0, 123.0));

    // 设置输入blob
    net.setInput(blob);

    // 运行前向传播，获取人脸检测结果
    Mat detections = net.forward();

    // 遍历检测结果
    for (int i = 0; i < detections.size[2]; i++)
    {
        float confidence = detections.at<float>(0, 0, i, 2);  // 获取置信度
        if (confidence > 0.5)  // 设置置信度阈值
        {
            // 计算人脸框的位置
            int startX = static_cast<int>(detections.at<float>(0, 0, i, 3) * image.cols);
            int startY = static_cast<int>(detections.at<float>(0, 0, i, 4) * image.rows);
            int endX = static_cast<int>(detections.at<float>(0, 0, i, 5) * image.cols);
            int endY = static_cast<int>(detections.at<float>(0, 0, i, 6) * image.rows);

            // 绘制人脸框和置信度
            String text = format("%.2f%%", confidence * 100);
            int y = startY - 10 > 10 ? startY - 10 : startY + 10;
            rectangle(image, Point(startX, startY), Point(endX, endY), Scalar(0, 255, 0), 2);
            putText(image, text, Point(startX, y), FONT_HERSHEY_SIMPLEX, 0.45, Scalar(0, 255, 0), 2);
        }
    }

    // 显示输出图像
    imshow("Output", image);
    waitKey(0);
    destroyAllWindows();

    return 0;
}