Á¤º¸°úÇÐȸ ³í¹®Áö B : ¼ÒÇÁÆ®¿þ¾î ¹× ÀÀ¿ë
ÇѱÛÁ¦¸ñ(Korean Title) |
ÆÛÁö ¼Ò¼Óµµ¸¦ °®´Â Fisherface ¹æ¹ýÀ» ÀÌ¿ëÇÑ ¾ó±¼ÀÎ½Ä |
¿µ¹®Á¦¸ñ(English Title) |
Face Recognition using Fisherface Method with Fuzzy Membership Degree |
ÀúÀÚ(Author) |
°û±Ùâ
°íÇöÁÖ
Àü¸í±Ù
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 31 NO. 06 PP. 0784 ~ 0791 (2004. 06) |
Çѱ۳»¿ë (Korean Abstract) |
º» ³í¹®¿¡¼´Â ÆÛÁö³í¸®¿¡ ±âÃÊÇÑ Fisherface ¾ó±¼ÀÎ½Ä ¹æ¹ýÀÇ È®ÀåÀ» ´Ù·é´Ù. Fisherface ¾ó±¼ÀÎ½Ä ¹æ¹ýÀº ÁÖ¼ººÐ ºÐ¼® ±â¹ý¸¸À» ÀÌ¿ëÇÏ´Â °æ¿ì¿¡ ºñÇØ Á¶¸íÀÇ ¹æÇâ, ¾ó±¼ÀÇ Æ÷Áî, °¨Á¤°ú °°Àº º¯µ¿¿¡ ´ëÇØ ¹Î°¨ÇÏÁö ¾ÊÀº ÀåÁ¡À» °¡Áö°í ÀÖ´Ù. ±×·¯³ª, Fisherface ¹æ¹ýÀ» Æ÷ÇÔÇÑ ¾ó±¼ÀνÄÀÇ ´Ù¾çÇÑ ¹æ¹ýµéÀº ÀÔ·Â º¤ÅÍ°¡ ÇÑ Å¬·¡½º¿¡ ÇÒ´çµÇ¾îÁú ¶§ ±× Ŭ·¡½º¿¡¼ ¼Ò¼ÓÀÇ Á¤µµ¸¦ 0 ¶Ç´Â 1·Î¼ ³ªÅ¸³½´Ù. µû¶ó¼ ÀÌ·¯ÇÑ ¹æ¹ýµéÀº ¾ó±¼¿µ»óµéÀÌ Á¶¸íÀ̳ª º¸´Â °¢µµ·Î ÀÎÇØ º¯ÇüÀÌ »ý±â´Â °æ¿ì¿¡ ÀνķüÀÌ ÀúÇϵǴ ¹®Á¦°¡ ÀÖ´Ù. º» ³í¹®¿¡¼´Â PCA¿¡ ÀÇÇØ º¯È¯µÈ Ư¡º¤ÅÍ¿¡ ÆÛÁö ¼Ò¼Óµµ¸¦ ÇÒ´çÇÏ´Â °ÍÀ¸·Î, ÆÛÁö ¼Ò¼Óµµ´Â ÆÛÁö kNN(k-Nearest Neighbor)À¸·ÎºÎÅÍ ¾ò¾îÁø´Ù. ½ÇÇè °á°ú ORL, Yale ¾ó±¼ µ¥ÀÌŸº£À̽º¿¡¼ ±âÁ¸ÀÇ ÀνĹæ¹ý º¸´Ù Çâ»óµÈ ÀÎ½Ä ¼º´ÉÀ» º¸ÀÓÀ» ¾Ë ¼ö ÀÖ¾ú´Ù.
|
¿µ¹®³»¿ë (English Abstract) |
In this study, we deal with face recognition using fuzzy-based Fisherface method. The well-known Fisherface method is more insensitive to large variation in light direction, face pose, and facial expression than Principal Component Analysis method. Usually, the various methods of face recognition including Fisherface method give equal importance in determining the face to be recognized, regardless of typicalness. The main point here is that the proposed method assigns a feature vector transformed by PCA to fuzzy membership rather than assigning the vector to particular class. In this method, fuzzy membership degrees are obtained from FKNN(Fuzzy K-Nearest Neighbor) initialization. Experimental results show better recognition performance than other methods for ORL and Yale face databases., |
Å°¿öµå(Keyword) |
¿µ»óó¸®
¾ó±¼ÀνÄ
ÆÛÁö KNN
¼±ÇüÆǺ°ºÐ¼®
ÁÖ¼ººÐ ºÐ¼®±â¹ý
°íÀ¯¾ó±¼
face recognition
LDA
PCA
eigenface
|
ÆÄÀÏ÷ºÎ |
PDF ´Ù¿î·Îµå
|