Исследование и разработка алгоритмов распознавания лиц
Обзор существующих алгоритмов для обнаружения лиц. Выравнивание лица с помощью разнообразных фильтров. Использование каскадного классификатора Хаара для поиска лиц на изображении. Распознавание лиц людей с использованием локальных бинарных шаблонов.
| Рубрика | Программирование, компьютеры и кибернетика |
| Вид | дипломная работа |
| Язык | русский |
| Дата добавления | 30.09.2016 |
| Размер файла | 332,4 K |
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Приложение 3. Листинг
face_recognizer.cpp
#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/opencv.hpp"
#include <cctype>
#include <iostream>
#include <iterator>
#include <stdio.h>
#include <dirent.h>
#include <unistd.h>
#include <fstream>
#include <cerrno>
#include <sstream>
#include <math.h>
#include <algorithm> // std::sort
using namespace cv;
using namespace std;
string cascadeName = "/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml";
Rect detectAndCut(Mat &img, CascadeClassifier& cascade, bool normalize = true)
{
vector<Rect> faces;
if (normalize) {
cvtColor(img, img, COLOR_BGR2GRAY);
resize(img, img, img.size(), 0, 0, INTER_LINEAR);
equalizeHist(img, img);
}
cascade.detectMultiScale(img, faces,
1.1, 2, 0
|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|CASCADE_SCALE_IMAGE
,
Size(30, 30));
for(vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++)
{
return *r;
}
throw "No faces were found";
}
static void fill_vectors(Mat& etalon_image, Mat& candidate_image, vector<Mat>& images, vector<int>& labels) {
images.push_back(etalon_image);
labels.push_back(0);
images.push_back(candidate_image);
labels.push_back(0);
}
struct Sort {
bool operator() (Rect i, Rect j) {
return (i.height > j.height);
}
} mySort;
vector<Rect> getTheBiggestEyes(vector<Rect> eyes) {
if (eyes.size() <= 2) {
return eyes;
}
vector<Rect> realEyes;
sort(eyes.begin(), eyes.end(), mySort);
for (int i = 0; i < 2; i++) {
realEyes.push_back(eyes.at(i));
}
return realEyes;
}
Mat alignByEyes(Mat image, Rect face) {
// preparation
Mat smallImgROI = image(face);
const char *cascade_eye = "/usr/share/opencv/haarcascades/haarcascade_eye.xml";
CascadeClassifier eyesClassifier;
vector<Rect> eyes;
if(!eyesClassifier.load(cascade_eye))
{
cerr << "ERROR: Could not load classifier cascade" << endl;
exit(-1);
}
eyesClassifier.detectMultiScale(smallImgROI, eyes,
1.2, 3, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
//|CASCADE_SCALE_IMAGE
,
Size(15, 15));
eyes = getTheBiggestEyes(eyes);
// for(vector<Rect>::const_iterator r = eyes.begin(); r != eyes.end(); r++) {
// CvPoint p1 = { face.x + r->x, face.y + r->y };
// CvPoint p2 = { face.x + r->x + r->width, face.y + r->y + r->height };
// rectangle(image, p1, p2, CV_RGB(0, 255, 0), 1, 4, 0);
// printf(" %d %d %d %d\n", face.x + r->x, face.y + r->y, r->width, r->height);
// }
if (eyes.size() == 2) {
int xHalfEye[2], yHalfEye[2];
int i = 0;
for(vector<Rect>::const_iterator r = eyes.begin(); r != eyes.end(); r++, i++) {
xHalfEye[i] = face.x + r->x + r->width / 2;
yHalfEye[i] = face.y + r->y + r->height / 2;
}
// rotation
double rotateAngle = atan((double)(yHalfEye[0] - yHalfEye[1]) / (double)(xHalfEye[0] - xHalfEye[1]))
* 180 / M_PI;
Point2f pc(image.cols/2., image.rows/2.);
Mat r = getRotationMatrix2D(pc, rotateAngle, 1.0);
warpAffine(image, image, r, image.size());
CascadeClassifier cascade;
if(!cascade.load(cascadeName)) {
cerr << "ERROR: Could not load classifier cascade" << endl;
exit(-1);
}
try {
face = detectAndCut(image, cascade, false);
} catch (char const* msg) {
cout << msg << endl;
exit(-1);
}
smallImgROI = image(face);
eyesClassifier.detectMultiScale(smallImgROI, eyes,
1.2, 3, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
//|CASCADE_SCALE_IMAGE
,
Size(15, 15));
eyes = getTheBiggestEyes(eyes);
// for(vector<Rect>::const_iterator r = eyes.begin(); r != eyes.end(); r++) {
// CvPoint p1 = { face.x + r->x, face.y + r->y };
// CvPoint p2 = { face.x + r->x + r->width, face.y + r->y + r->height };
// rectangle(image, p1, p2, CV_RGB(0, 255, 0), 1, 4, 0);
// printf(" %d %d %d %d\n", r->x, r->y, r->width, r->height);
// }
if (eyes.size() == 2) {
int xHalfEye[2], yHalfEye[2];
int i = 0;
for(vector<Rect>::const_iterator r = eyes.begin(); r != eyes.end(); r++, i++) {
xHalfEye[i] = face.x + r->x + r->width / 2;
yHalfEye[i] = face.y + r->y + r->height / 2;
}
// cropping
int currentBetweenPupilsDistance = abs(xHalfEye[0] - xHalfEye[1]);
int currentLeftEyeX = xHalfEye[0] < xHalfEye[1] ? xHalfEye[0] : xHalfEye[1];
Rect myROI;
myROI.x = currentLeftEyeX - 0.89 * currentBetweenPupilsDistance;
myROI.width = 2.78 * currentBetweenPupilsDistance;
myROI.y = yHalfEye[0] - 0.39 * myROI.width;
myROI.height = myROI.width;
image = image(myROI);
//resize(image, image, Size(200, 200));
}
}
return image;
}
int main(int argc, const char *argv[]) {
if (argc != 3) {
cout << "usage: " << argv[0] << " <etalon_path> <candidate_path>" << endl;
exit(1);
}
string etalon = string(argv[1]);
string candidate = string(argv[2]);
CascadeClassifier cascade;
if(!cascade.load(cascadeName))
{
cerr << "ERROR: Could not load classifier cascade" << endl;
return -1;
}
Mat etalon_image, candidate_image;
Rect etalonFace, candidateFace;
etalon_image = imread(etalon, 1);
try {
etalonFace = detectAndCut(etalon_image, cascade);
} catch (char const* msg) {
cout << msg << endl;
return -1;
}
etalon_image = alignByEyes(etalon_image, etalonFace);
candidate_image = imread(candidate, 1);
try {
candidateFace = detectAndCut(candidate_image, cascade);
} catch (char const* msg) {
cout << msg << endl;
return -1;
}
candidate_image = alignByEyes(candidate_image, candidateFace);
int medianBlurFilterIntensity = 3;
int gaussianBlurFilterIntensity = medianBlurFilterIntensity;
medianBlur(etalon_image, etalon_image, medianBlurFilterIntensity);
medianBlur(candidate_image, candidate_image, medianBlurFilterIntensity);
GaussianBlur(etalon_image, etalon_image, Size(gaussianBlurFilterIntensity, gaussianBlurFilterIntensity), 0);
GaussianBlur(candidate_image, candidate_image, Size(gaussianBlurFilterIntensity, gaussianBlurFilterIntensity), 0);
// namedWindow("named_window");
// imshow("named_window", etalon_image);
// waitKey();
// imshow("named_window", candidate_image);
// waitKey();
vector<Mat> images;
vector<int> labels;
fill_vectors(etalon_image, candidate_image, images, labels);
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
Mat testSample = images[images.size() - 1];
images.pop_back();
labels.pop_back();
Ptr<FaceRecognizer> model = createLBPHFaceRecognizer(2, 8, 6, 6); // try call with params: 2, 8, 6, 6
model->train(images, labels);
int predictedLabel = model->predict(testSample);
double confidence = 0.0;
model->predict(testSample, predictedLabel, confidence);
cout << confidence << endl;
return 0;
}
Calculator.java
import java.util.ArrayList;
public class Calculator {
private float xAverageTheirs, standardDeviationTheirs, xAverageStrangers, standardDeviationStrangers;
private ArrayList<ArrayList<String>> source;
public Calculator(ArrayList<ArrayList<String>> source) {
this.source = source;
}
public void calculate(int group) {
float sumTheirs = 0, sumStrangers = 0;
int quantityTheirs = 0, quantityStrangers = 0;
for (ArrayList<String> row : source) {
if (row.get(0).equals(row.get(1))) {
sumTheirs += Float.parseFloat(row.get(group));
quantityTheirs++;
} else {
sumStrangers += Float.parseFloat(row.get(group));
quantityStrangers++;
}
}
xAverageTheirs = sumTheirs / quantityTheirs;
xAverageStrangers = sumStrangers / quantityStrangers;
float numeratorTheirs = 0, numeratorStrangers = 0;
for (ArrayList<String> row : source) {
if (row.get(0).equals(row.get(1))) {
float xMinusXAvg = Float.parseFloat(row.get(group)) - xAverageTheirs;
numeratorTheirs += xMinusXAvg * xMinusXAvg;
} else {
float xMinusXAvg = Float.parseFloat(row.get(group)) - xAverageStrangers;
numeratorStrangers += xMinusXAvg * xMinusXAvg;
}
}
standardDeviationTheirs = (float)Math.sqrt(numeratorTheirs / quantityTheirs);
standardDeviationStrangers = (float)Math.sqrt(numeratorStrangers / quantityStrangers);
}
public float getXAverageTheirs() {
return xAverageTheirs;
}
public float getStandardDeviationTheirs() {
return standardDeviationTheirs;
}
public float getXAverageStrangers() {
return xAverageStrangers;
}
public float getStandardDeviationStrangers() {
return standardDeviationStrangers;
}
}
Client.java
public class Client
{
public static String interimStatisticsFilePath = null;
public static String folderWithPhotos = null;
public static String executableFilename = null;
public static void main (String args[])
{
interimStatisticsFilePath = args[0];
folderWithPhotos = args[1];
executableFilename = args[2];
Recognizer r = new Recognizer();
r.recognize();
// r.recognizeShuffle();
Statistics s = new Statistics();
s.gather();
}
}
IMatcher.java
public interface IMatcher
{
final static float NO_FACE_ETALON = -1;
final static float NO_FACE_CANDIDATE = -2;
final static float INTERNAL_ERROR = -3;
final static float NO_SUCH_FILE = -4;
public float compare(String etalon, String candidate) throws Exception;
}
OpenCVMatcher.java
import java.io.BufferedReader;
import java.io.InputStreamReader;
public class OpenCVMatcher implements IMatcher
{
private String etalon, candidate;
private float extractAndMatch() throws Exception {
Process p;
String command = null;
String result = null;
try {
command = Client.executableFilename + " " + etalon + " " + candidate;
p = Runtime.getRuntime().exec(command);
String line = "";
BufferedReader errors = new BufferedReader(new InputStreamReader(p.getErrorStream()));
BufferedReader output = new BufferedReader(new InputStreamReader(p.getInputStream()));
while ((line = output.readLine()) != null) {
result = line;
}
while ((errors.readLine()) != null) {}
p.waitFor();
} catch (Exception e) {
throw e;
}
try {
return Float.parseFloat(result);
} catch (NumberFormatException e) {
return -1;
}
}
public float compare(String etalon, String candidate) throws Exception
{
this.etalon = etalon;
this.candidate = candidate;
return extractAndMatch();
}
}
Parser.java
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.nio.charset.Charset;
import java.util.ArrayList;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
public class Parser {
public static ArrayList<ArrayList<String>> parse(String filepath, int[] groups) throws Exception {
String line;
try (
InputStream fis = new FileInputStream(filepath);
InputStreamReader isr = new InputStreamReader(fis, Charset.forName("UTF-8"));
BufferedReader br = new BufferedReader(isr);
) {
ArrayList<ArrayList<String>> results = new ArrayList<ArrayList<String>>();
Pattern pattern = Pattern.compile("^(.+);(.+);(.+);(.+);(.+)$", Pattern.MULTILINE);
while ((line = br.readLine()) != null) {
Matcher m = null;
m = pattern.matcher(line);
m.find();
ArrayList<String> temp = new ArrayList<String>();
for(int group : groups) {
temp.add(m.group(group));
}
results.add(temp);
}
return results;
}
}
}
Percentager.java
public class Percentager
{
public static void main (String args[])
{
String etalonFilename = "/home/mihail/diploma/aberdeen_face_database/amellanby15.jpg";
String candidateFilename = "/home/mihail/diploma/aberdeen_face_database/andrew7.jpg";
Client.executableFilename = "/home/mihail/workspace/binary/face_recognizer";
IMatcher opencvMatcher = new OpenCVMatcher();
float resultOpenCV = 0, resultOpenCV1 = 0, resultOpenCV2 = 0;
try {
resultOpenCV1 = opencvMatcher.compare(etalonFilename, candidateFilename);
resultOpenCV2 = opencvMatcher.compare(candidateFilename, etalonFilename);
resultOpenCV = (resultOpenCV1 + resultOpenCV2) / 2;
} catch (Exception e) {
// TODO Auto-generated catch block
System.out.println(e.getMessage());
}
//System.out.println("Confidence: " + getPercentFromDistance(resultOpenCV) + "%");
System.out.println("These people are identical: " + getAnswerFromDistance(resultOpenCV));
}
private static String getAnswerFromDistance(float resultOpenCV) {
if (resultOpenCV < 20)
return "true";
else if (resultOpenCV >= 26)
return "false";
else
return "not sure";
}
private static int getPercentFromDistance(float resultOpenCV) {
//distance thresholds
int absolutelyTrueThreshold = 13;
int trueThreshold = 46;
int notSureThreshold = 177;
int falseThreshold = 501;
//percent thresholds
int absolutelyTruePercent = 99;
int truePercent = 70;
int notSurePercent = 30;
int falsePercent = 0;
//steps
double absolutelyTrueStep = 1d / absolutelyTrueThreshold;
double trueStep = 29d / (trueThreshold - absolutelyTrueThreshold);
double notSureStep = 40d / (notSureThreshold - trueThreshold);
double falseStep = 30d / (falseThreshold - notSureThreshold);
//logic
double confidencePercent = 0;
if (resultOpenCV > notSureThreshold) {
confidencePercent = falsePercent + ((falseThreshold - resultOpenCV) * falseStep);
} else if (resultOpenCV > trueThreshold) {
confidencePercent = notSurePercent + ((notSureThreshold - resultOpenCV) * notSureStep);
} else if (resultOpenCV > absolutelyTrueThreshold) {
confidencePercent = truePercent + ((trueThreshold - resultOpenCV) * trueStep);
} else {
confidencePercent = absolutelyTruePercent + ((absolutelyTrueThreshold - resultOpenCV) * absolutelyTrueStep);
}
return (int)confidencePercent;
}
}
PhotosContainer.java
public class PhotosContainer
{
private int label;
private String filename;
public PhotosContainer(int label, String filename)
{
this.label = label;
this.filename = filename;
}
public int getLabel()
{
return label;
}
public String getFilename()
{
return filename;
}
}
Recognizer.java
import java.io.File;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Random;
public class Recognizer {
List<PhotosContainer> photoNames = new ArrayList<PhotosContainer>();
public void listFilesForFolder(final File folder) {
for (final File fileEntry : folder.listFiles()) {
if (fileEntry.isDirectory()) {
listFilesForFolder(fileEntry);
} else {
photoNames.add(new PhotosContainer(Integer.parseInt(folder.getName()),
Client.folderWithPhotos + '/' + folder.getName() + '/' + fileEntry.getName()));
}
}
}
public void recognizeShuffle() {
final File folder = new File(Client.folderWithPhotos);
try {
compare(folder);
} catch (Exception e) {
e.getMessage();
}
}
private void compare(File folder) throws Exception {
File[] pathsList = folder.listFiles();
Random rand = new Random();
for (int i = 0; i < 5; i++) {
File randFolder = pathsList[rand.nextInt(pathsList.length)];
File[] fileList = randFolder.listFiles();
File randEtalonFile = fileList[rand.nextInt(fileList.length)];
File randCandidateFile = fileList[rand.nextInt(fileList.length)];
while (randEtalonFile.getName().equals(randCandidateFile.getName())) {
randCandidateFile = fileList[rand.nextInt(fileList.length)];
}
IMatcher opencvMatcher = new OpenCVMatcher();
float resultOpenCV1 = opencvMatcher.compare(randEtalonFile.getAbsolutePath(), randCandidateFile.getAbsolutePath());
float resultOpenCV2 = opencvMatcher.compare(randCandidateFile.getAbsolutePath(), randEtalonFile.getAbsolutePath());
float resultOpenCV = (resultOpenCV1 + resultOpenCV2) / 2;
System.out.println(randEtalonFile.getAbsolutePath() + " and " + randCandidateFile.getAbsolutePath()
+ " are identical: " + getAnswerFromDistance(resultOpenCV) + " (distance = " + resultOpenCV + ")");
}
for (int i = 0; i < 5; i++) {
File randEtalonFolder = pathsList[rand.nextInt(pathsList.length)];
File[] fileList = randEtalonFolder.listFiles();
File randEtalonFile = fileList[rand.nextInt(fileList.length)];
File randCandidateFolder = pathsList[rand.nextInt(pathsList.length)];
fileList = randCandidateFolder.listFiles();
File randCandidateFile = fileList[rand.nextInt(fileList.length)];
while (randEtalonFile.getName().equals(randCandidateFile.getName())) {
randCandidateFile = fileList[rand.nextInt(fileList.length)];
}
IMatcher opencvMatcher = new OpenCVMatcher();
float resultOpenCV1 = opencvMatcher.compare(randEtalonFile.getAbsolutePath(), randCandidateFile.getAbsolutePath());
float resultOpenCV2 = opencvMatcher.compare(randCandidateFile.getAbsolutePath(), randEtalonFile.getAbsolutePath());
float resultOpenCV = (resultOpenCV1 + resultOpenCV2) / 2;
System.out.println(randEtalonFile.getAbsolutePath() + " and " + randCandidateFile.getAbsolutePath()
+ " are identical: " + getAnswerFromDistance(resultOpenCV) + " (distance = " + resultOpenCV + ")");
}
}
private static String getAnswerFromDistance(float resultOpenCV) {
if (resultOpenCV == -1) {
return "no faces were found";
} else if (resultOpenCV < 28)
return "true";
else if (resultOpenCV >= 28)
return "false";
else
return "not sure";
}
public void recognize() {
try (PrintWriter writer = new PrintWriter(Client.interimStatisticsFilePath, "UTF-8")) {
final File folder = new File(Client.folderWithPhotos);
listFilesForFolder(folder);
int count = (photoNames.size() * photoNames.size() - photoNames.size()) / 2;
int i = 0;
for (Iterator<PhotosContainer> iterator = photoNames.iterator(); iterator.hasNext();) {
PhotosContainer etalon = iterator.next();
for (PhotosContainer candidate : photoNames) {
if (!etalon.getFilename().equals(candidate.getFilename())) {
// System.out.println(etalon.getLabel() + ";" + candidate.getLabel() + ";" + etalon.getFilename() +
// ";" + candidate.getFilename());
IMatcher opencvMatcher = new OpenCVMatcher();
float resultOpenCV1 = opencvMatcher.compare(etalon.getFilename(), candidate.getFilename());
float resultOpenCV2 = opencvMatcher.compare(candidate.getFilename(), etalon.getFilename());
float resultOpenCV = (resultOpenCV1 + resultOpenCV2) / 2;
// IMatcher openbrMatcher = new OpenBRMatcher();
// float resultOpenBR = openbrMatcher.compare(etalon.getFilename(), candidate.getFilename());
// System.out.println(etalon.getLabel() + ";" + candidate.getLabel() + ";" + etalon.getFilename() +
// ";" + candidate.getFilename() + ";" + resultOpenCV);
writer.println(etalon.getLabel() + ";" + candidate.getLabel() + ";" + etalon.getFilename() +
";" + candidate.getFilename() + ";" + resultOpenCV);
i++; System.out.println(((float)i/count*100) + "%");
}
}
iterator.remove();
}
/* Exceptions processing */
} catch (Exception e) {
//some exception message
e.printStackTrace();
}
}
}
Statistics.java
import java.util.ArrayList;
public class Statistics {
public void gather() {
int []groupsToResult = {1, 2, 5};
ArrayList<ArrayList<String>> results = null;
try {
results = Parser.parse(Client.interimStatisticsFilePath, groupsToResult);
} catch (Exception e) {
System.out.println("Parser fucked up");
}
Calculator calculator = new Calculator(results);
calculator.calculate(2);
System.out.println(calculator.getXAverageTheirs() + " - " + calculator.getXAverageStrangers() + "; "
+ calculator.getStandardDeviationTheirs() + " - " + calculator.getStandardDeviationStrangers());
// calculator.calculate(3);
// System.out.println(calculator.getXAverageTheirs() + " - " + calculator.getXAverageStrangers() + "; "
// + calculator.getStandardDeviationTheirs() + " - " + calculator.getStandardDeviationStrangers());
// calculator.calculate(4);
// System.out.println(calculator.getXAverageTheirs() + " - " + calculator.getXAverageStrangers() + "; "
// + calculator.getStandardDeviationTheirs() + " - " + calculator.getStandardDeviationStrangers());
}
}
Time.java
public class Time {
final static long SECOND = 1000;
final static long MINUTE = 60 * Time.SECOND;
final static long HOUR = 60 * Time.MINUTE;
}
VisionLabsMatcher.java
import static java.nio.file.StandardCopyOption.REPLACE_EXISTING;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
public class VisionLabsMatcher implements IMatcher
{
private final String pathToEtalonTemp = "C:/temp/etalon_temp";
private final String pathToCandidateTemp = "C:/temp/candidate_temp";
private final String pathToEtalonKeys = "C:/temp/etalon_keys";
private final String pathToCandidateKeys = "C:/temp/candidate_keys";
private final String pathToReport = "C:/temp/report.csv";
private final static String HASP_FEATURE_EXPIRED = "HASP_FEATURE_EXPIRED";
private final static String NO_FACES_FOUND = "NoFacesFound";
private float extractAndMatch() throws Exception {
Process p;
String command = null;
try {
command = VisionLabsMatcher.class.getProtectionDomain().getCodeSource().getLocation().getPath()
+ "/../Extractor -S " + pathToEtalonTemp + " -D " + pathToEtalonKeys + " -Dt DPM -St CNN -Ft VGG";
p = Runtime.getRuntime().exec(command);
String line = "";
BufferedReader errors = new BufferedReader(new InputStreamReader(p.getErrorStream()));
BufferedReader output = new BufferedReader(new InputStreamReader(p.getInputStream()));
while ((output.readLine()) != null) {}
while ((line = errors.readLine()) != null) {
if (line.contains(HASP_FEATURE_EXPIRED)) {
throw new Exception(HASP_FEATURE_EXPIRED);
} else if (line.contains(NO_FACES_FOUND)) {
return NO_FACE_ETALON;
} else {
return INTERNAL_ERROR;
}
}
p.waitFor();
command = VisionLabsMatcher.class.getProtectionDomain().getCodeSource().getLocation().getPath()
+ "/../Extractor -S " + pathToCandidateTemp + " -D " + pathToCandidateKeys + " -Dt DPM -St CNN -Ft VGG";
p = Runtime.getRuntime().exec(command);
errors = new BufferedReader(new InputStreamReader(p.getErrorStream()));
output = new BufferedReader(new InputStreamReader(p.getInputStream()));
while ((output.readLine()) != null) {}
while ((line = errors.readLine()) != null) {
return NO_FACE_CANDIDATE;
}
p.waitFor();
command = VisionLabsMatcher.class.getProtectionDomain().getCodeSource().getLocation().getPath()
+ "/../Matcher2 -T " + pathToEtalonKeys + " -C " + pathToCandidateKeys + " -D " + pathToReport + " -M 1 -St CNN";
p = Runtime.getRuntime().exec(command);
errors = new BufferedReader(new InputStreamReader(p.getErrorStream()));
output = new BufferedReader(new InputStreamReader(p.getInputStream()));
while ((output.readLine()) != null) {}
while ((line = errors.readLine()) != null) {
return INTERNAL_ERROR;
}
p.waitFor();
} catch (Exception e) {
throw e;
}
return parseResult();
}
private float parseResult() {
Pattern pattern = Pattern.compile("^(.+); (.+); (.+); (.+)$",
Pattern.MULTILINE);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(pathToReport));
String line = null;
Matcher m = null;
while ((line = reader.readLine()) != null) {
try {
m = pattern.matcher(line);
m.find();
} catch (Exception e) {
return INTERNAL_ERROR;
}
return Float.parseFloat(m.group(4));
}
} catch (Exception e) {
return INTERNAL_ERROR;
} finally {
try {
reader.close();
} catch (IOException e) {
}
}
return (float) -1;
}
public float compare (String etalon, String candidate) throws Exception
{
float result = NO_SUCH_FILE;
if(prepare(etalon, candidate)) {
result = extractAndMatch();
};
return result;
}
private boolean prepare(String etalon, String candidate) {
File etalonTemp = new File(pathToEtalonTemp);
etalonTemp.mkdir();
File candidateTemp = new File(pathToCandidateTemp);
candidateTemp.mkdir();
File etalonKeys = new File(pathToEtalonKeys);
etalonKeys.mkdir();
File candidateKeys = new File(pathToCandidateKeys);
candidateKeys.mkdir();
File sourceEtalon = new File(etalon);
File sourceCandidate = new File(candidate);
Path etalonTempPath = Paths.get(pathToEtalonTemp + "/etalon.jpg");
Path candidateTempPath = Paths.get(pathToCandidateTemp + "/candidate.jpg");
try {
Files.copy(sourceEtalon.toPath(), etalonTempPath, REPLACE_EXISTING);
Files.copy(sourceCandidate.toPath(), candidateTempPath, REPLACE_EXISTING);
} catch (IOException e) {
return false;
}
return true;
}
}
Список литературы
1) International Journal of Computer Vision 57(2), 137 - 154, 2004.
2) Journal of Signal and Information Processing, 173 - 175, 2013.
3) IJCSNS International Journal of Computer Science and Network Security, Vol.10 No.6, 2010.
4) M. Turk and A. Pentland, “Eigenfaces for Recognition”, Journal of Cognitive Neuroscience, Vol.3, No.1, 71 - 86, 1991.
5) International Journal of Advanced Technology in Engineering and Science Vol.1, Issue No.12, 2013.
6) Jigar M. Pandya, Devang Rathod, Jigna J. Jadav, “A Survey of Face Recognition approach”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol.3, Issue 1, 632, 2013.
7) Jyoti S. Bedre, Shubhangi Sapkal, “Comparative Study of Face Recognition Techniques: A Review”, Emerging Trends in Computer Science and Information Technology - 2012 (ETCSIT2012) Proceedings published in International Journal of Computer Applications (IJCA), 2012.
8) Mohammed Javed, Bhaskar Gupta, “Performance Comparison of Various Face Detection Techniques”, International Journal of Scientific Research Engineering & Technology (IJSRET) Vol.2 Issue No.1, 19 - 27, 2013.
9) J. R. Beveridge, P. J. Phillips, G. H. Givens, B. A. Draper, M. N. Teli, and D. S. Bolme, “When high-quality face images match poorly”, Proceedings, Ninth International Conference on Automatic Face and Gesture Recognition, 2011.
10) O. Barkan, J. Weill, L. Wolf, and H. Aronowitz. “Fast high dimensional vector multiplication face recognition”, ICCV, 2013.
11) T. Berg and P. N. Belhumeur. “Tom-vs-pete classifiers and identity-preserving alignment for face verification”, BMVC, 2012.
12) G. B. Huang, M. A. Mattar, H. Lee, and E. G. Learned-Miller. “Learning to align from scratch”, NIPS, 773 - 781, 2012.
13) Y. Taigman and L. Wolf. “Leveraging billions of faces to overcome performance barriers in unconstrained face recognition”, 2011.
14) Голубев М. Н. “Использование информации о цвете в алгоритме выделения лиц на базе бустинга”, Сб. науч. труд. 13-й всерос. науч.-техн. конф. “Нейроинформатика - 2011”, М, 2011. Ч. 3. С. 55 - 62.
15) Unsang Park, “Face Recognition: face in video, age invariance, and facial marks”, Michigan State University, 2009.
16) T. Ahonen, A. Hadid and M. Pietikainen, “Face description with Local Binary Patterns”, Application to Face Recognition. Machine Vision Group, University of Oulu, Finland, 2006.
17) T. Ahonen, A. Hadid, M. Pietikainen and T. Maenpaa. “Face recognition based on the appearance of local regions”, Proceedings of the 17th International Conference on Pattern Recognition, 2004.
18) R. Gottumukkal and V.K. Asari, “An Improved Face Recognition Technique Based on Modular PCA Approach”, Pattern Recognition Letters, Vol.25, 429 - 436, 2004.
19) T. Ojala, M. Pietikainen and D. Harwood, “A comparative study of texture measures with classification based on feature distributions”, Pattern Recognition Vol.29, 1996.
20) M. Turk and A. Pentland, “Eigenfaces for recognition”, Cognitive Neuroscience, 72 - 86, 1991.
21) M. Kirby and L. Sirovich, “Application of the Karhunen-Loeve procedure for the characterization of human faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 103 - 108, 1990.
22) W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A literature survey”, ACM Computing Surveys (CSUR), 399 - 458, 2003.
23) S. Z. Li and A. K. Jain, “Handbook of Face Recognition”, Springer-Verlag, Secaucus, NJ, 2005.
24) W. Zhao and R. Chellappa “Robust face recognition using symmetric shape from-shading”, Technical Report, Center for Automation Research, University of Maryland, 1999.
25) T. Chen, Y. Wotao, S. Z. Xiang, D. Comaniciu, and T. S. Huang, “Total variation models for variable lighting face recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1519 - 1524, 2006.
26) M. Grudin, “On internal representation in face recognition systems”, Pattern Recognition, 1161 - 1177, 2000.
27) P. A. Viola and M. J. Jones, “Robust real-time face detection”, International Journal of Computer Vision, 137 - 154, 2004.
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