YEREL İKİLİ ÖRÜNTÜ VE YÖNLÜ GRADYANT HİSTOGRAMI KULLANILARAK YÜZ GÖRÜNTÜLERİNDEN CİNSİYET TAHMİNİ

Yıl 2018, , 14 - 22, 31.01.2018

Tariq Khalıfa Gökhan Şengül

https://doi.org/10.28948/ngumuh.383746

Cited By: 2

Öz

   Yüz görüntülerinden cinsiyet tahmini;
insan-bilgisayar arayüzü, müşteri bilgilerinin ölçülmesi, erişim kontrolü gibi
birçok alanda kullanılmaktadır. Bunlara ek olarak cinsiyet tahminin; güvenlik
sistemleri, biyometrik kimlik doğrulama, medikal görüntüleme sistemleri,
demografik çalışmalar, içerik tabanlı arama, izleme sistemleri gibi alanlarda
da uygulanma potansiyeli bulunmaktadır. Bu çalışmada yüz görüntülerinden
cinsiyet tahmini için yerel ikili örüntü (local binary patterns) ve yönlü
gradyant histogramını özellik çıkarıcı ve sınıflandırıcı olarak da k-en Yakın Komşuluk ve Destek Vektör
Makinelerini kullanan bir sistem önerilmiştir. Önerilen sistem FERET ve UTD
veritabanlarında test edilmiştir. Testler esnasında birini dışarıda bırakma
çapraz geçerleme tekniği uygulanmıştır. Elde edilen sonuçlar tatmin edici
seviyededir. 

Anahtar Kelimeler

Yüz görüntüleri, cinsiyet tahmini, yerel ikili örüntü, yönlü granyant histogramı

GENDER PREDICTION FROM FACIAL IMAGES USING LOCAL BINARY PATTERNS AND HISTOGRAMS OF ORIENTED GRADIENTS TRANSFORMATIONS

Öz

   Gender
prediction from facial images can be used in a large number of applications
including human-computer interaction, customer information measurement, access
control, etc. Furthermore, it can substantially effect on many fields, such as
security systems, biometric authentication, medical imaging systems,
demographic studies, content based searching, and surveillance system. In this
study, we proposed to use Local Binary Patterns (LBP) and Histograms of
Oriented Gradients (HOG) as the feature extractor and k-Nearest Neighbor
(k-NN) and Support Vector Machine (SVM) as the classifier in order to
predict the gender of the people from facial images. We tested the proposed
method in FERET and UTD databases. We used leave-one-out approach as the cross
validation technique. The results are promising.

Anahtar Kelimeler

Facial images, gender prediction, Local Binary Patterns, Histograms of Oriented Gradients

Kaynakça

• [1] GUTTA, S., WECHSLER, H., PHILLIPS, P.J., “Gender and Ethnic Classification of Face İmages” Proceedings of Third IEEE International Conference on In Automatic Face and Gesture Recognition, 194-199. New York, USA 1998.
• [2] SUN, Z., BEBIS, G., YUAN, X., LOUIS, S.J., “Genetic Feature Subset Selection for Gender Classification: A comparison Study”, In Proceedings o Sixth IEEE Workshop on Applications of Computer Vision, 165-170. Orlando, USA, 2002.
• [3] MOGHADDAM, B., YANG, M.H. , “Learning Gender with Support Faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 707-711, 2010.
• [4] JAIN, A., HUANG, J., “Integrating Independent Components and Linear Discriminant Analysis for Gender Classification ”, In Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 159-163. Seoul, South Korea, 2004.
• [5] PHILLIPS, P.J., FERET (face recognition technology) Recognition Algorithm Development and Test Results. Army Research Laboratory, London, UK, 1996.
• [6] COSTEN, N.P., BROWN, M., AKAMATSU, S., “Sparse Models for Gender Classification”, In Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 201-206. Seoul, South Korea, 2004..
• [7] SUN, N., ZHENG, W., SUN, C., ZOU, C., ZHAO, L, “Gender Classification Based on Boosting Local Binary Pattern”, Advances in Neural Networks-ISNN,1, 194-201, 2006.
• [8] LIAN, H.C., LU, B.L., “Multi-view Gender Classification using Local Binary Patterns and Support Vector Machines”, In Advances in Neural Networks-ISNN,1,202-209, 2006.
• [9] MÄKINEN, E., RAISAMO, R., “An Experimental Comparison of Gender Classification Methods”, Pattern Recognition Letters, 29(10), 1544-1556, 2006.
• [10] FANG, Y., WANG, Z., “Improving LBP Features for Gender Classification”, In International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR'08, 373-377. Hong Kong, China, 2008. ‏
• [11] SCALZO, F., BEBIS, G., NICOLESCU, M., LOSS, L., TAVAKKOLI, A., “Feature fusion Hierarchies for Gender Classification”, In 19th International Conference on Pattern Recognition, 1-4. FL, USA, 2008.
• [12] ZAFEIRIOU, S., TEFAS, A., PITAS, I., “Gender Determination using a Support Vector Machine Variant”, In 16th European Signal Processing Conference, 1-5. Lausanne, Switzerland, 2008.
• [13] MESSER, K., MATAS, J., KITTLER, J., LUETTIN, J.,MAITRE, G., “XM2VTSDB: The Extended M2VTS Database”, In Second international conference on audio and video-based biometric person authentication, 965-966. Guildford, UK, 2008.
• [14] LU, L., SHI, P., “A Novel Fusion-Based Method for Expression-Invariant Gender Classification” In IEEE International Conference on Acoustics, Speech and Signal Processing, 1065-1068. Taipei, Taiwan, 2009.
• [15] SINGH, V., SHOKEEN, V., SINGH, M.B., “Comparison of Feature Extraction Algorithms for Gender Classification from Face Images”, In International Journal of Engineering Research and Technology, 1, 2-5, 2013.
• [16] LIU, H., GAO, Y., WANG, C., “Gender Identification in Unconstrained Scenarios using Self-Similarity of Gradients Features”, In IEEE International Conference on Image Processing 5911-5915. Paris, France, 2014.
• [17] LIN, G.S., ZHAO, Y.J., “A feature-based Gender Recognition Method Based on Color Information”, In First International Conference on Robot, Vision and Signal Processing, 40-43. Kaohsiung, Taiwan, 2011.
• [18] SHAN, C., “Learning Local Binary Patterns for Gender Classification on Real-World Face Images”, Pattern Recognition Letters, 33(4), 431-437, 2012.
• [19] ULLAH, I., HUSSAIN, M., MUHAMMAD, G., ABOALSAMH, H., BEBIS, G., MIRZA, A.M., “Gender Recognition from Face Images with Local Wld Descriptor”, In 19th International Conference on Systems,. 417-420. Vienna, Austria, 2012.
• [20] WANG, C., HUANG, D., WANG, Y., ZHANG, G., “Facial Image-Based Gender Classification using Local Circular Patterns”, In 21st International Conference on Pattern Recognition (ICPR), 2432-2435. Tsukuba, Japan, 2012.
• [21] LI, M., BAO, S., DONG, W., WANG, Y., SU, Z., “Head-Shoulder Based Gender Recognition”, In 20th IEEE International Conference on Image Processing (ICIP), 2753-2756. Melbourne, VIC, Australia, 2013.
• [22] DEY, E.K., KHAN, M., ALI, M.H., “Computer Vision-Based Gender Detection from Facial Image”, International Journal of Advanced Computer Science, 3(8), 428-433, 2013.
• [23] IGA, R., IZUMI, K., HAYASHI, H., FUKANO, G., OHTANI, T. “A Gender and Age Estimation System from Face Images” SICE Annual Conference, 22-24. Fukui, Japan, 2003.
• [24] YE, J., ZHAN, Y., SONG, S., “Facial Expression Features Extraction Based on Gabor Wavelet Transformation”, In IEEE International Conference on Systems, Man and Cybernetics, 2215-2219. Hague, Netherlands, 2004.
• [25] YANG Z., AI, H., “Demographic Classification with Local Binary Patterns”, In Int. Conf. on Biometrics, 464–473. Berlin, Heidelberg, 2007.
• [26] GUO, G., MU, G., “Human age Estimation: What is the Influence Across Race and Gender?”, In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 71-78. San Francisco, USA, 2010.
• [27] RICANEK J.R.K., TESAFAYE, T., “Morph: A Longitudinal Image Database of Normal Adult Age-Progression”, In 7th International Conference on Automatic Face and Gesture Recognition, 341-345. Southampton, UK, 2006.
• [28] YAN, S., XU, D., ZHANG, B., ZHANG, H.J., YANG, Q., LIN, S., “Graph Embedding and Extensions: a General Framework for Dimensionality Reduction”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(1), 40-51, 2007.
• [29] CAI, D., HE, X., HAN, J., ZHANG, H.J., “Orthogonal Laplacianfaces for Face Recognition”, Image Processing, IEEE Transactions on, 15(11), 3608-3614, 2006.
• [30] CAI, D., HE, X., ZHOU, K., HAN, J., BAO, H., “Locality Sensitive Discriminant Analysis”, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 708-713. Hyderabad, India, 2007.
• [31] SHIRKEY, D.M., GUPTA, S.R., “An Image Mining System for Gender Classification & Age Prediction Based on Facial Features”, International Journal of Science and Modern Enginnering (IJISME), 1(6), 8-12, 2013.
• [32] FAZL-ERSI, E., MOUSA-PASANDI, M.E., LAGANIERE, R., AWAD, M., “Age and Gender Recognition using Informative Features of Various Types”, In IEEE International Conference on Image Processing 5891-5895. Paris, France, 2014.
• [33] GALLAGHER, A., CHEN, T., “Understanding Images of Groups of People”, In IEEE Conference on Computer Vision and Pattern Recognition, 256-263. Miami, USA, 2009.
• [34] JAIN, A.K., DUIN, R.P., MAO, J., “Statistical Pattern Recognition: A review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4-37, 2000.
• [35] DALAL, N., TRIGGS, B., “Histograms of Oriented Gradients for Human Detection”, In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 886-893. San Diego, USA, 2005.
• [36] OJALA, T., PIETIKÄINEN, M., MÄENPÄÄ, T. “Multiresolution gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns”, Ieee Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987, 2002.
• [37] SENGUL, G., “Classification of Parasite Egg Cells Using Gray Level Cooccurence Matrix and kNN”, Biomedical Research – India, 27(3), 829-834, 2016.
• [38] VAPNIK, V., The nature of Statistical Learning Theory. Springer Science & Business Media, London, UK, 2013.
• [39] TAPKIN, S., ŞENGÖZ, B., ŞENGÜL, G., TOPAL, A., ÖZÇELİK, E., “Estimation of Polypropylene Concentration of Modified Bitumen Images by using k-NN and SVM Classifiers”, Journal of Computing in Civil Engineering, 29(5), 04014055-1-11, 2013.
• [40] VIOLA, P., JONES, M.J., “Robust Real-Time Face Detection”, International Journal of Computer Vision, 57(2), 137-154, 2004.
• [41] ROWLEY, H., BALUJA, S., KANADE, T., “Neural Network-Based Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23-38, 1998.
• [42] SCHNEIDERMAN, H., KANADE, T., “A Statistical Method for 3D Object Detection Applied to Faces and Cars”, In IEEE Conference on Computer Vision and Pattern Recognition, 746-751. Hilton Head Island, SC, USA, 2000.
• [43] ROTH, D., YANG, M. H., AHUJA, N., “A SNoW-Based Face Detector”, Advances in Neural Information Processing Systems 12, 862-868. Denver, USA, 1999.