基於隱馬爾可夫模型的人臉識別C/C++源代碼

基於隱馬爾可夫模型的人臉識別C/C++源代碼
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將生物特徵識別應用於人臉,實際上是包含兩個方面:第一,從圖像或視頻幀中檢測人臉,即所謂的"人臉
檢測"(face detection);第二,對檢測到的人臉進行識別,判斷這張臉是誰,即"人臉識別"(face recognition).
就實際應用而言,採用人臉做生物特徵識別,其識別率,可靠性都無法與指紋,虹膜識別相提並論,但失
爲模式識別中的一個典型應用,至少可以起到拋磚引玉的作用.
下面的源代碼採用隱馬爾可夫模型(HMM)做人臉識別,它是OPENCV 3.1 版本的一個應用示例程序,
不再包含在 4.0 版本中.因此如果想編譯源代碼,則需要安裝 OPENCV 3.1版本.該版本可以從
SOURCEFORG上下載.關於程序使用以及算法說明,參考下面的網頁(英文):
http://www.assuredigit.com/incoming/sourcecode/opencv/chinese_docs/appPage/FaceRecognition/FaceRecognitio
n.htm
以及論文:
http://www.assuredigit.com/incoming/sourcecode/opencv/chinese_docs/papers/avbpa99.pdf
下載地址:
http://www.assuredigit.com/program/HMMDemo.rar
(轉自阿須數碼)
稿件B: 人臉檢測的C/C++源代碼
人臉檢測的C/C++源代碼,曾發表於 OPENCV 的 MAILING LIST,主要是對OPENCV 3.1 版本發佈的代
碼做了一些速度上的優化,並且解決了內存泄漏的問題.這個程序所使用的 Paul Viola 提出(該論文"Rapid
Object Detection using a Boosted Cascade of Simple Features"發表在 CVPR'01)的 Ada Boosted Cascade 算法
可以說是目前最好最快的目標檢測算法.
關於OPENCV的介紹,參考:
http://blog.csdn.net/hunnish/archive/2004/09/13/102535.aspx
關於該算法的詳細介紹,也可參考:
http://www.merl.com/people/viola/research/publications/CVPR-2001.pdf
中國視覺網 http://www.china-vision.net/
以及:
http://www.assuredigit.com/forum/display_topic_threads.asp ForumID=11&TopicID=325
http://www.assuredigit.com/forum/display_topic_threads.asp ForumID=11&TopicID=463
運行文件下載:
http://www.assuredigit.com/product_tech/Demo_Download_files/Face.exe
該程序可以對靜止圖像以及視頻序列進行 face tracking.對視頻序列,請先插入USB接口的攝像頭.
====
在OPENCV 3.1 版本,VC6.0下編譯通過
====
===
#ifdef _CH_
#define WIN32
#error "The file needs cvaux, which is not wrapped yet. Sorry"
#endif
#ifndef _EiC
#include "cv.h"
#include "cvaux.h"
#include "highgui.h"
#endif
#ifdef _EiC
#define WIN32
#endif
#define ORIG_WIN_SIZE 24
static CvMemStorage* storage = 0;
static CvHidHaarClassifierCascade* hid_cascade = 0;
#define WINNAME "Result"
void detect_and_draw( IplImage* image, IplImage* TempImage );
int main( int argc, char** argv )
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{
CvCapture* capture = 0;
CvHaarClassifierCascade* cascade =
cvLoadHaarClassifierCascade( "",
cvSize( ORIG_WIN_SIZE, ORIG_WIN_SIZE ));
hid_cascade = cvCreateHidHaarClassifierCascade( cascade, 0, 0, 0, 1 );
cvReleaseHaarClassifierCascade( &cascade );
cvNamedWindow( WINNAME, 1 );
storage = cvCreateMemStorage(0);
if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))
capture = cvCaptureFromCAM( argc == 2 argv[1][0] - '0' : 0 );
else if( argc == 2 )
capture = cvCaptureFromAVI( argv[1] );
if( capture )
{
IplImage *frame, *temp;
cvGrabFrame( capture );
frame = cvRetrieveFrame( capture );
temp = cvCreateImage( cvSize(frame->width/2,frame->height/2), 8, 3 );
for(;;)
{
if( !cvGrabFrame( capture ))
break;
frame = cvRetrieveFrame( capture );
if( !frame )
break;
detect_and_draw( frame, temp );
if( cvWaitKey( 10 ) >= 0 )
{
//cvReleaseImage( &frame );
//cvReleaseImage( &temp );
cvReleaseCapture( &capture );
cvDestroyWindow(WINNAME);
return 0;
}
}
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}
else
{
char* filename = argc == 2 argv[1] : (char*)"lena.jpg";
IplImage* image = cvLoadImage( filename, 1 );
IplImage* temp = cvCreateImage( cvSize(image->width/2,image->height/2), 8, 3 );
if( image )
{
cvFlip( image, image, 0 );
image->origin = IPL_ORIGIN_BL;
detect_and_draw( image, temp );
cvWaitKey(0);
cvReleaseImage( &image );
cvReleaseImage( &temp );
}
cvDestroyWindow(WINNAME);
return 0;
}
return 0;
}
void detect_and_draw( IplImage* img, IplImage* temp )
{
int scale = 2;
CvPoint pt1, pt2;
int i;
cvPyrDown( img, temp, CV_GAUSSIAN_5x5 );
#ifdef WIN32
cvFlip( temp, temp, 0 );
#endif
cvClearMemStorage( storage );
if( hid_cascade )
{
CvSeq* faces = cvHaarDetectObjects( temp, hid_cascade, storage,
1.2, 2, CV_HAAR_DO_CANNY_PRUNING );
for( i = 0; i total : 0); i++ )
{
CvRect* r = (CvRect*)cvGetSeqElem( faces, i, 0 );
pt1.x = r->x*scale;
pt2.x = (r->x+r->width)*scale;
#ifdef WIN32
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pt1.y = img->height - r->y*scale;
pt2.y = img->height - (r->y+r->height)*scale;
#else
pt1.y = r->y*scale;
pt2.y = (r->y+r->height)*scale;
#endif
cvRectangle( img, pt1, pt2, CV_RGB(255,255,0), 3 );
}
}
cvShowImage(WINNAME, img );
//cvReleaseImage( &temp );
(轉自阿須數碼)
}
#ifdef _EiC
main(1,"facedetect.c");
#endif
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