Face Recognition Based on Coarse Sub-bands of Contourlet Transformation and Principal Component Analysis

Abstract

In this paper, a face recognition system, by using Contourlet transform (CT) as a two dimensional discrete transform and principal component analysis (PCA) as a sub-space method to form the feature vectors, is implemented. Any input image is decomposed by CT up to three levels and the CT coefficients are obtained at three scales and 15 orientations. The obtained CT coefficients are used by PCA to form the feature vectors. At the end, the Euclidean distance is used for classification. Our experimental results on ORL data base show the appropriate performance in comparison with other approaches even though for each subject only one image is used for training and other 9 images are used for testing. The average accuracy of our proposed algorithm for face recognition is 96.07%. äººè¸è¯å«åºäºContourletåæ¢å主æååæçç²å­å¸¦ä¼å°åå§åå¸ç±³é²¥é±¼ï¼ æ½è±¡ å¨æ¬æ中ï¼é¢é¨è¯å«ç³»ç»ï¼éè¿ä½¿ç¨è½®å»æ³¢åæ¢ï¼CTï¼ï¼å¶ä¸ºäºç»´ç¦»æ£åæ¢å主æååæï¼PCAï¼ï¼ä¸ºå­ç©ºé´æ³ï¼ä»¥å½¢æç¹å¾åéï¼å®ç°çãä»»ä½è¾å¥å¾è±¡æ¯éè¿CTå解æå¤ä¸ä¸ªæ°´å¹³åä¸ä¸ªå°ºåº¦å15ååè·å¾çCTç³»æ°ãå°æå¾çCTç³»æ°ç±PCAç¨äºå½¢æç¹å¾åéãå¨ç»ææ¶ï¼æ¬§å éå¾·è·ç¦»è¢«ç¨äºåç±»ãæ们å¨ORLæ°æ®çåºç¡çå®éªç»æ表æï¼ä¸å¶ä»çæ¹æ³ï¼å³ä½¿å¯¹äºæ¯ä¸ªåè¯èä»ä¸ä¸ªå¾åç¨äºå¹è®­åå¶ä»9å¾åç¨äºæµè¯æ¯è¾çéå½çæ§è½ãæ们æåºçç®æ³ç¨äºäººè¸è¯å«çå¹³å精度为96.07ï¼