OpenCV4.0實現人臉識別

概述

OpenCV4.0深度神經網絡模塊，支持openface模型的導入，提取人臉的128特徵向量，進行相似度比對，實現人臉識別。Openface模型的詳細信息看這裡

http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf

主要原理是基於2015年CVPR的FaceNet網絡的論文，去年的時候寫過一篇文章介紹過它，想要了解詳細信息的點擊這裡查看即可

OpenCV+Tensorflow實現實時人臉識別演示

主要思路

首先使用OpenCV4.0 DNN模塊支持的人臉檢測模型，實現對圖像或者視頻的人臉檢測，然後對得到的人臉區域通過openface的預訓練模型提取128個特徵向量值，基於餘弦相似度進行特徵值比對，實現人臉識別。完整的流程可以圖示如下：

餘弦相似公式與解釋：

代碼實現步驟

01

加載網絡

需要先加載人臉檢測與openface人臉識別網絡模型，代碼實現如下：

 String modelDesc = "D:/projects/opencv_tutorial/data/models/resnet/deploy.prototxt";
 String modelBinary = "D:/projects/opencv_tutorial/data/models/resnet/res10_300x300_ssd_iter_140000.caffemodel";
 String facemodel = "D:/projects/opencv_tutorial/data/models/face_detector/openface.nn4.small2.v1.t7";
 // 初始化網絡
 Net net = readNetFromCaffe(modelDesc, modelBinary);
 Net netRecogn = readNetFromTorch(facemodel);

這兩個模型的下載地址如下：

https://github.com/gloomyfish1998/opencv_tutorial/tree/master/data/models/face_detector

02

設置計算後臺

OpenCV支持不同的計算後臺，這裡我們採用OpenVINO作為計算後臺，可以實現加速計算，代碼如下：

// 設置計算後臺
Net netRecogn = readNetFromTorch(facemodel);
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(DNN_TARGET_CPU);
netRecogn.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
netRecogn.setPreferableTarget(DNN_TARGET_CPU);
// load face data
vector<vector<float>> face_data;
vector<string> labels;
vector<string> faces;
glob("D:/my_faces/zhigang", faces);
for (auto fn : faces) {
 vector<float> fv;
 Mat sample = imread(fn);
 recognize_face(sample, netRecogn, fv);
 face_data.push_back(fv);
 printf("file name : %s\n", fn.c_str());
 labels.push_back("zhigang");
}
faces.clear();
glob("D:/my_faces/balvin", faces);
for (auto fn : faces) {
 vector<float> fv;
 Mat sample = imread(fn);
 recognize_face(sample, netRecogn, fv);
 face_data.push_back(fv);
 printf("file name : %s\n", fn.c_str());
 labels.push_back("balvin");
}
if (net.empty() || netRecogn.empty())
{
 printf("could not load net...\n");
 return -1;
}

03

人臉檢測

通過人臉檢測網絡實現人臉檢測，代碼實現如下：

// 輸入數據調整
Mat inputBlob = blobFromImage(frame, inScaleFactor,
 Size(inWidth, inHeight), meanVal, false, false);
net.setInput(inputBlob, "data");
// 人臉檢測
Mat detection = net.forward("detection_out");
vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());

04

人臉比對

把實時檢測得到ROI區域與預先加載的人臉樣本進行比較，找到距離最小的，如果小於閾值T，即為識別輸出結果，解析人臉檢測並實現人臉識別的代碼如下：

for (int i = 0; i < detectionMat.rows; i++)
{
 // 置信度 0～1之間
 float confidence = detectionMat.at<float>(i, 2);
 if (confidence > confidenceThreshold)
 {
 int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
 int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
 int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
 int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
 Rect object((int)xLeftBottom, (int)yLeftBottom,
 (int)(xRightTop - xLeftBottom),
 (int)(yRightTop - yLeftBottom));
 if (object.width < 5 || object.height < 5) {
 continue;
 }
 // 截取人臉ROI區域
 Mat roi = frame(object);
 // 人臉比對，發現相似度最高的
 vector<float> curr_fv;
 recognize_face(roi, netRecogn, curr_fv);
 float minDist = 10;
 int index = -1;
 for (int i = 0; i < face_data.size(); i++) {
 float dist = compare(curr_fv, face_data[i]);
 if (minDist > dist) {
 minDist = dist;
 index = i;
 }
 }
 // 閾值與顯示識別結果
 printf("index : %d, dist: %.2f \n", index, minDist);
 if (index >= 0 && minDist < 0.30) {
 putText(frame, labels[index].c_str(), Point(xLeftBottom, yLeftBottom-20),
 FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255, 0, 255));
 }
 rectangle(frame, object, Scalar(0, 255, 0));
 ss.str("");
 ss << confidence;
 String conf(ss.str());
 String label = "Face: " + conf;
 int baseLine = 0;
 Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
 rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
 Size(labelSize.width, labelSize.height + baseLine)),
 Scalar(255, 255, 255), FILLED);
 putText(frame, label, Point(xLeftBottom, yLeftBottom),
 FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
 }
}

餘弦相似比較

float compare(vector<float> &fv1, vector<float> fv2) {
 // 計算餘弦相似, 0 ~ 1 距離，距離越小越相似，
 // 0表示夾角為0°，1表示夾角為90°
 float dot = 0;
 float sum2 = 0;
 float sum3 = 0;
 for (int i = 0; i < fv1.size(); i++) {
 dot += fv1[i] * fv2[i];
 sum2 += pow(fv1[i], 2);
 sum3 += pow(fv2[i], 2);
 }
 float norm = sqrt(sum2)*sqrt(sum3);
 float similarity = dot / norm;
 float dist = acos(similarity) / CV_PI;
 return dist;
}

運行效果