Facial images dimensionality reduction and recognition by means of 2DKLT

• Autorzy: Kukharev G. , Forczmański P.
• Języki publikacji: EN

Abstrakty

EN

Paper presents an efficient dimensionality reduction method for images (e.g. human faces databases). It does not require any usual pre-processing stage (like down-scaling or filtering). Its main advantage is associated with efficient representation of images leading to accurate recognition. Analysis is performed using two-dimensional Principal Component Analysis and Linear Discriminant Analysis and reduction by means of two-dimensional Karhunen-Loeve Transform. The paper presents mathematical principles together with some results of recognition experiments on popular facial databases. The experiments performed on several facial image databases (BioID [11], ORL/AT&T [3], FERET [8], Face94 [4] and Face95 [5]) showed that face recognition using this type of feature space dimensionality reduction is particularly convenient and efficient, giving high recognition performance.

Słowa kluczowe

EN

image recognition; image compression; dimensionality reduction

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2007
• Tom: Vol. 16, No. 3/4
• Strony: 401--425
• Opis fizyczny: Bibliogr. 31 poz., il., tab., wykr.

Twórcy

autor

Kukharev G.

autor

Forczmański P.

• Szczecin University of Technology, Faculty of Computer Science and Information Systems, Zolnierska Str. 49, 71-210 Szczecin, Poland,

Bibliografia

• [1] Fukunaga K.: Introduction to statistical Pattern Recognition, 2nd edition. New York, Academic Press, 1990.
• [2] Turk M., Pentland A.: Eigenfaces for recognition. Journal of Cognitive Neurosicence, Vol. 3, No. 1, pp. 71-86, 1991.
• [3] AT&T Laboratories Cambridge. Database of faces, http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html, 1994.
• [4] University of Essex, Department of Computer Science. Essex Face94 facial images collection 1994. http://cswww.essex.ac.uk/mv/allfaces/faces94.html
• [5] University of Essex, Department of Computer Science. Essex Face95 facial images collection. http://cswww.essex.ac.uk/mv/allfaces/faces95.html
• [6] Swets D. L., Weng J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 831-836, 1996.
• [7] Tsapatsoulis N., Alexopoulos V., Kollias S.: A vector based approximation of KLT and Its application to face recognition. Proceedings of The IX European Signal Processing Conference EUSIPCO-98. Rhodos Palace, Island of Rhodes, Greece, 1998.
• [8] Phillips P., Wechsler H., Huang J., Rauss P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing, 16/5, pp. 295-306, 1999.
• [9] Swets D. L., Weng J.: Hierarchical discriminant analysis for image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 5, pp. 386-401, 1999.
• [10] Chen Li-Fen, Liao Hong-Yuan M., Ming-Tat K., Ja-Chen L., Gwo-Jong Y.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition, Vol. 33. pp. 1713-1726, 2000.
• [11] BioID-Technology Research. The BioID Face Database, 2001. http://www.bioid.com/doMnloads/facedb/index.php
• [12] Kukharev G., Forczmański P.: Hierarchical method of reduction of features dimensionality for image recognition and graphical data retrieval. Proceedings of Sixth International Conference Pattern Recognition and Information Processing, Minsk, Belarus, May, pp. 19-34, 2001.
• [13] Yu H., Yang J.: A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recognition., Vol. 34, pp. 2067-2070, 2001.
• [14] Kukharev G., Kuzminski A.: Biometric techniques. Part I. The Methods of Face Recognition (in Polish). Szczecin (Poland), Pracownia Poligraficzna WI PS, 310 p., 2003.
• [15] Annadurai S., Saradha A.: Discrete cosine transform based face recognition using linear discriminant analysis. IJSIT Lecture Note of International Conference on Intelligent Knowledge Systems, Vol. 1, No. 1, 2004.
• [16] Chen Songcan, Liu Jun, Zhou Zhi-Hua.: Making FLDA applicable to face recognition with one sample per person. Pattern Recognition, 37(7), pp. 1553-1555, 2004.
• [17] Kukharev G., Forczmański P.: Data dimensionality reduction for face recognition. Machine Graphics and Vision, Vol. 13, No. 1/2, 2004.
• [18] Yang J., Zhang D., Frangi A. F., Yang Jing-Yu: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26 (1) pp. 131-137, 2004.
• [19] Chen Songcan, Zhu Yulian, Zhang Daoqiang, Yang Jing-Yu: Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, 26, pp. 1157-1167, 2005.
• [20] Jing Xiao-Yuan, Tang Yuan-Yan, Zhang David: A fourier-LDA approach for image recognition, Pattern Recognition, vol. 38, pp. 453-457, 2005.
• [21] Kukharev G., Forczmański P.: Face recognition by means of two-dimensional direct linear disriminant analysis. Proceedings of 8th International Conference on Pattern Recognition and Information Processing, Minsk, Belarus, May 18-20, 2005.
• [22] Li Stan Z., Jain Anil K.: Handbook of face recignition, Springer, 395p., 2005.
• [23] Li Ming, Yuan Baozong: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, 26. pp. 527 -532, 2005.
• [24] Zhang Daoqiang, Chen Songcan, Liu Ju: Representing image matrices: eigenimages versus eigenvectors. Lecture Notes in Computer Science, Advances in Neural Networks, Volume 3497, pp. 659-664, 2005.
• [25] Duin Robert P. W., Loog M. O., Ho Tin K.: Recent submissions in linear dimensionality reduction and face recognition / Editorial, Pattern Recognition Letters 27, pp. 707-708, 2006.
• [26] Jing Xiao-Yuan, Wong Hau-San, Zhang D.: Face recognition based on 2D Fisherface approach, Pattern Recognition, vol.39, pp. 707-710, 2006.
• [27] Kukharev G., Forczmański P.: Two-dimensional LDA approach to image compression and recognition. Computing, Multimedia and Intelligent Techniques, Częstochowa University of Technology, Poland, vol. 2 No. 1, pp. 45–53, 2006.
• [28] Kukharev G., Szczegolewa N.: Face recognition systems (in Russian). Sankt-Petersburg, LETI, Russia, 170 p., 2006.
• [29] Nagabhushan P., Guru D. S., Shekar B. H.: Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach. Pattern Recognition 39(4) pp. 721-725, 2006.
• [30] Noushath S., Hemantha Kumar G., Shivakumara P.: (2D)2LDA: An efficient approach for face recognition. Pattern Recognition, vol. 39. pp. 1396-1400, 2006.
• [31] Forczmański P., Kukharev G.: Comparative analysis of simple facial features extractors. Journal of Real-Time Image Processing, vol. 1, No. 4 / July, pp. 239-255, 2007.