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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Current Result Document : 6 / 7 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) PCA¿Í SVM¿¡ ±â¹ÝÇÏ´Â ºü¸¥ ¾ó±¼ ŽÁö ¹æ¹ý
¿µ¹®Á¦¸ñ(English Title) A Fast Method for Face Detection Based on PCA and SVM
ÀúÀÚ(Author) ÇÏÃá·Ú   ½ÅÇö°©   ¹Ú¸íö. Çϼ®¿î   Chun-Lei Xia   Hyeon-Gab Shin   Myeong-Chul Park   Seok-Wun Ha  
¿ø¹®¼ö·Ïó(Citation) VOL 11 NO. 06 PP. 1129 ~ 1135 (2007. 06)
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(Korean Abstract)
¾ó±¼ÀÎ½Ä ±â¼úÀº ÄÄÇ»ÅÍ ºñÀü ºÐ¾ß¿¡¼­ Áß¿äÇÑ ¿ªÇÒÀ» ´ã´çÇÏ°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â£¬ PCA¿Í SVM ±â¼úÀ» »ç¿ëÇÏ´Â ºü¸¥ ¾ó±¼Àνıâ¼úÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÑ ½Ã½ºÅÛ¿¡¼­´Â£¬ ¸ÕÀú Áö¿ª È÷½ºÅä±×·¥ ºÐÆ÷¸¦ ºÐ¼®ÇÏ¿© »ý¼ºÇÑ Åë°èÀû Ư¼ºÀ» »ç¿ëÇÔÀ¸·Î½á ¾ó±¼°¡´É¿µ¿ªÀ» ÇÊÅ͸µÇÑ´Ù. ÀÌ °úÁ¤¿¡¼­ ´ëºÎºÐÀÇ ºñ¾ó±¼ ¿µ¿ªÀÌ Á¦°ÅµÇ±â ¶§¹®¿¡ ŽÁö °úÁ¤ÀÇ Ã³¸®¼Óµµ°¡ Çâ»óµÈ´Ù. ´ÙÀ½À¸·Î´Â PCA Ư¡ º¤ÅÍ°¡ »ý¼ºµÇ°í£¬SVM ºÐ·ù±â¸¦ »ç¿ëÇÏ¿© Å×½ºÆ® ¿µ»ó ³»¿¡ ¾ó±¼ÀÌ Á¸ÀçÇÏ´ÂÁö¸¦ ŽÁöÇÑ´Ù. º» ³í¹®¿¡¼­ÀÇ Å×½ºÆ® ¿µ»óÀº CMU ¾ó±¼ µ¥ÀÌÅͺ£À̽º¸¦ »ç¿ëÇÏ¿´À¸¸ç£¬SVMÀÇ ÇнÀ À» À§ÇÑ ¾ó±¼°ú ºñ¾ó±¼ »ùÇõéÀº MIT µ¥ÀÌÅÍ ¼¼Æ®·ÎºÎÅÍ ¼±ÅÃÇÏ¿´´Ù. ¾ó±¼Å½Áö ½ÇÇè°á°ú£¬ Á¦¾ÈÇÑ ¹æ¹ý¿¡¼­ ÁÁÀº ¼º´ÉÀ» ³ªÅ¸³»¾ú´Ù.
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(English Abstract)
Human face detection technique plays an important role in computer vision area. It has lots of applications such as face recognition, video surveillance, human computer interface, face image database management, and querying image databases. In this paper, a fast face detection approach using Principal Component Analysis (PCA) and Support Vector Machines (SVM) is proposed based on the previous study on face detection technique. In the proposed detection system, firstly it filter the face potential area using statistical feature which is generated by analyzing the local histogram distribution, the detection process is speeded up by eliminating most of the non-face area in this step. In the next step£¬PCA feature vectors are generated, and then detect whether there are faces present in the test image using SVM classifier. Finally, store the detection results and output the results on the test image. The test images in this paper are from CMU face database. The face and non-face samples arc selected from the MIT data set. The experimental results indicate the proposed method has good performance for face detection.
Å°¿öµå(Keyword) SVM   PCA   histogram distribution   face potential area  
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