LBP Yardımıyla Görüntüdeki Kişinin Yaşının Bulunması

Yıl 2011, Cilt: 8 Sayı: 1, - , 01.05.2011

Vasif V. Nabiyev , Asuman Günay

Öz

Age estimation from facial images and facial age progression is crucial in
security systems design. In this study local binary pattern (LBP) histograms are used to
classify the age from facial images. The LBP operator is an effective texture descriptor and
used in the fields of texture classification, segmentation, face detection, face recognition
and gender estimation. The local binary patterns (LBP) are fundamental properties of
local image texture and the occurrence histogram of these patterns is an effective texture
feature for face description. In the study the faces are divided into small regions from
which the LBP histograms are extracted and concatenated into a feature vector to be used
as an efficient face descriptor. For every new face presented to the system, spatial LBP histograms are produced and used to classify the image into one of the age classes. In the
classification phase we use minimum distance, nearest neighbor and k-nearest neighbor
classifiers. The distances between the samples are calculated with Euclidean, normalized
Euclidean, chi-square and weighted chi-square distances. The experimental results have
shown that system performance is %89 for age estimation.

Anahtar Kelimeler

Age estimation, local binary patterns (LBP), classification

Kaynakça

• [1] A. Lanitis, C. J. Taylor and T. F. Cootes, Toward automatic simulation of aging effects on face images, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002), 442-455.
• [2] A. F. Valinskiy, Kriminoloji, Yur nauki, Russia 1999.
• [3] J. Hayashi, M. Yasumoto, H. Ito and H. Koshimizu, Age and gender estimation based on wrinkle texture and color of facial images, Proceedings of the 16th International Conference on Pattern Recognition (ICPR’02) 1 (2002), 405–408.
• [4] Y. H. Kwon and N. da Vitoria Lobo, Age classification from facial images, Computer Vision and Image Understanding 74 (1999), 1–21.
• [5] S. Mukaida and H. Ando, Extraction and manipulation of wrinkles and spots for facial image synthesis, Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04) (2004), 749–754.
• [6] C. Choi, Age change for predicting future faces, 1999 IEEE International Fuzzy Systems Conference (FUZZ-IEEE’99) Proceedings 3 (1999), 1603–1608.
• [7] R. Iga, K. Izumi, H. Hayashi, G. Fukano and T. Ohtani, A gender and age estimation system from face images, SICE Annual Conference in Fukui, Fukui University, Japan 1 (2003), 756– 761.
• [8] T. Kawano, K. Kato and K. Yamamoto, An analysis of the gender and age differentiation using facial parts, 2005 IEEE International Conference on Systems, Man and Cybernetics 4 (2005), 3432–3436.
• [9] N. Ramanathan and R. Chellappa, Face verification across age progression, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) 2 (2005), 462–469.
• [10] T. Ahonen, A. Hadid and M. Pietikainen, Face recognition with local binary patterns, ECCV 2004, LNCS 3021 (2004), 469–481.
• [11] A. Hadid, M. Pietikainen and T. Ahonen, A discriminative feature space for detecting and recognizing faces, 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04) 2 (2004), 797–804.
• [12] H-C Lian and B-L Lu, Multi-view gender classification using local binary patterns and support vector machines, ISNN (2)’06, LNCS 3972 (2006), 202–209.
• [13] L. He, C. Zou, L. Zhao and D. Hu, An enhanced LBP feature based on facial expression recognition, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (2006), 3300–3303.
• [14] J. M. Parramon, Bas ve Portre Cizme Sanatı, Remzi Kitabevi, Istanbul 2000.
• [15] T. Ojala, M. Pietikainen and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002), 971–987.
• [16] V. V. Nabiyev, Algoritmalar, Teoriden Uygulamalara, Seckin Yayıncılık, Ankara 2009.
• [17] P. J. Phillips, H. Wechsler, J. Huang and P. Rauss, The FERET database and evaluation procedure for face-recognition algorithms, Image and Vision Computing 16 (1998), 295–306.
• [18] P. J. Phillips, H. Moon, S.A. Rizvi and P. J. Rauss, The FERET evaluation methodology for face-recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000), 1090–1104.