ALTIGEN SAYISAL GÖRÜNTÜ İŞLEMEDE GRİ-SEVİYE EŞOLUŞUM MATRİSLERİ İLE YÜZ TANIMA

Yüz Tanıma, onlarca yıldır çekici bir araştırma alanı olmuştur, çünkü yüz en kullanışlı ve deterministik biyometrilerden biridir. Görüntü işleme, varlığından bu yana kare piksel tabanlı görüntü işleme olarak adlandırılır. Bununla birlikte, pikselleri altıgen olarak tasarlama ve işleme fikrine dayanan altıgen görüntü işlemenin, yapılan araştırmalar sonucunda, zaman ve bellek tasarrufu açısından önemli yararlar sağlayabilecceği görülmüştür. Şimdiye kadar ki önerilen ve hayata geçirilen yüz tanıma yöntemlerinin hemen hepsi kare piksel tabanlı görüntü işleme üzerine inşa edilmiştir. Altıgen piksel tabanlı görüntü işlemede yüz tanıma üzerine önerilmiş çalışma sayısının azlığına dayanarak, bu çalışmada, altıgen görüntü işleme tabanlı bir yüz tanıma yöntemi önerilmiştir. Bu çalışmada önerilen yöntemde, kare piksel tabanlı yüz tanıma yöntemlerinin en temellerinden biri olan Gri Seviye Eş-Oluşum Matrislerinden (GLCM) esinlenilmiştir. Yöntem, kare piksel tabanlı temel GLCM metoduna dayandığından, Hex_Direct_GLCM adı verilmiştir. Donanımsal olarak, altıgen piksel tabanlı görüntü işleme henüz mevcut olmadığından, kare piksel tabanlı sayısal görüntülerin, altıgen piksel tabanlı karşılıkları yazılım yoluyla yapay olarak oluşturulmuştur. Kare piksel tabanlı GLCM yönteminde takip edilen adımların altıgen piksel tabandaki karşılıkları gerçeklenmiş ve farklı veri setleri üzerinde yüz tanıma doğruluk performans analizi yapılmıştır. Simülasyon sonuçlarında sunulduğu üzere, Hex_Direct_GLCM yöntemi, hem zaman hem de yer gibi kaynakların tasarrufunda sağladığı başarımın yanı sıra, yüz tanıma açısından da yüksek doğruluk oranıyla rekabetçi sonuçlar vermektedir.

FACE RECOGNITION BY GREY-LEVEL CO-OCCURRENCE MATRICES IN HEXAGONAL DIGITAL IMAGE PROCESSING

Face Recognition has been an attractive field of research fordecades, because face is one of the most useful and deterministicbiometrics. Image processing is called square pixel-based imageprocessing since its existence. However, hexagonal image processing,which is based on the idea of designing and processing pixels ashexagons, has been shown to provide significant benefits in terms of timeand memory savings. Almost all of the face recognition methods proposedand implemented so far are based on square pixel based imageprocessing. Based on the limited number of studies on face recognitionin hexagonal pixel based image processing, a hexagonal image processingbased face recognition method is proposed in this study. The methodproposed in this study is inspired by Grey Level Co-occurrence Matrices(GLCM), which is one of the most fundamental of square pixel based facerecognition methods. The method is named Hex_Direct_GLCM because itis based on the square pixel-based basic GLCM method. Since hardwarebased hexagonal pixel-based image processing is not yet available,hexagonal pixel-based equivalents of square pixel-based digital imagesare artificially created by software. The hexagonal pixel base equivalentsof the steps followed in the GLCM method are performed, and then facerecognition accuracy performance analysis is performed on different datasets. As presented in the simulation results, the Hex_Direct_GLCMmethod provides competitive results with high accuracy in terms of facerecognition as well as the success in saving resources such as time andspace.

PDF

___

Adur, J., Carvalho, H.F., Cesar, C.L. (2014). Nonlinear Optical Microscopy Signal Processing Strategies in Cancer. Cancer Informatics, 13(13), p.67–76.
Ahonen, T., Hadid, A., Pietikainen, M. (2004). Face recognition with local binary patterns. In Proceedings of the 8th European Conference on Computer Vision (p. 469-481). Prague, Czech Republic.
Ali, A., Jing, X., Saleem, N. (2011). GLCM-Based Fingerprint Recognition Algorithm, In Proceedings of the 4th IEEE International Conference on Broadband Network and Multimedia Technology (p.207-211). Shenzhen, China.
Belhumeur, P., Hespanha, J., Kriegman, D. (1997). Eigenfaces vs. fisher-faces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), p.711- 720.
Bell, S.C.M., Holroyd, F.C., Mason, D.C. (1989). A digital geometry for hexagonal pixels. Image and Vision Computing, 7, p.194–204.
Chakraborty, S., Singh, S.K., Chakraborty, P. (2017). Local Directional Gradient Pattern: a Local Descriptor for Face Recognition. Multimedia Tools and Applications, 76, p.1201-1216.
Champion, I., Germain, C., Da Costa, J.P., Alborini, A., Dubois-Fernandez, P. (2014). Retrieval of Forest Stand Age from SAR Image Texture for Varying Distance and Orientation Values of the Grey Level Co-Occurrence Matrix. IEEE Geoscience and Remote Sensing Letters, 11(1), p.5– 9.
Comon, P. (1994). Independent component analysis - a new concept?. Signal Processing, 36, p.287- 314.
Dahmane, M., Meunier, J. (2011). Emotion recognition using dynamic grid based HoG features. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (p. 884–888). Santa Barbara, CA, USA
Dan, Z., Chen, Y., Yang, Z., Wu, G. (2014). An Improved Local Binary Pattern for Texture Classification. Optik, 125, p.6320-6324.
Dong, Y., Tao, D., Li, X. (2015). Nonnegative Multiresolution Representation-Based Texture Image Classification. ACM Transactions on Intelligent Systems and Technology, 7(3), p.1–21.
Dong, Y., Tao, D., Li, X., Ma, J., Pu, J. (2015). Texture Classification and Retrieval Using Shearlets and Linear Regression. IEEE Transactions on Cybernetics, 45(3), p.358–369.
Dubey, S.R. (2017). Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval. arXiv:1709.09518 [cs.CV].
Eleyan, A., Demirel, H. (2011). Co-occurrence matrix and its statistical features as a new approach for face recognition. Turk J Elec Eng & Comp Sci, 19, p.97-107.
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D. (2008). The CAS-PEAL LargeScale Chinese Face Database and Baseline Evaluations. IEEE Trans. on System Man, and Cybernetics (Part A), 38(1), p.149-161.
Guan, Z., Wang, C., Chen, Z., Bu, J., Chen, C. (2010). Efficient Face Recognition Using Tensor Subspace Regression. Neurocomputing, 3, p.2744-2753.
Hales, T.C. (2000). Cannonballs and honeycombs. Notices of the American Mathematical Society, 47(4), p.440–449.
Hales, T.C. (2001). The Honeycomb Conjecture. Discrete Computational Geometry, 25, p.1–22.
Haralick, R.M., Shanmugam, K., Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, p. 610-621.
Heikkilä, M., Pietikäinen, M., Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42(3), p. 425–436.
Jabid, T., Kabir, M.H., Chae, O. (2010). Robust facial expression recognition based on local directional pattern. ETRI Journal, 32(5), p.784–794.
Jafri, R., Arabnia, H.R. (2009). A Survey of Face Recognition Techniques. Journal of Information Processing Systems, 5(2), p.41-68.
Jain, A.K., and Ross, A. (2008). Introduction to Biometrics, A Handbook of Biometrics, Springer, p.1– 22.
Jain, A.K., Hong, L., Pankanti, S., (2000), Biometric Identification. Communications of the ACM, 43, p.91–98.
Lei, Z., Liao, S., Pietikainen, M. (2011). Face Recognition by Exploring Information jointly in Space, Scale, and Orientation. IEEE Transactions on Image Processing, 20(1), p.247-256.
Libor, S. (2000). Face Recognition Data.
Liu, L., Fieguth, P., Guo, Y., Wang, X., Pietikainen, M. (2017). Local Binary Features for Texture Classification: Taxonomy and experimental study. Pattern Recognition, 62, p.135-160.
Lyons, M.J., Akemastu, S. Kamachi, M., Gyoba, J. (1998). Coding Facial Expressions with Gabor Wavelets. In Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, p.200-205. Nara, Japan.
Melendez, J., Garcia, M.A., Puig, D. (2008). Efficient distance-based per-pixel texture classification with Gabor wavelet filters. Pattern Anal. Appl., 11(3), p.365–372.
Nisenson, M., Yariv, I., El-Yaniv, R., Meir, R. (2003). Towards Behaviometric Security Systems: Learning to Identify a Typist. Lecture Notes in Computer Science, p.363-374.
Omer, Y., Ertugrul, F. (2017). Gender classification from facial images using grey relational analysis with novel local binary pattern descriptors. Signal Image and Video Processing, 11, p.769–776.
Ou, X., Pan, W., Xiao, P. (2014). In vivo skin capacitive imaging analysis by using grey level cooccurrence matrix (GLCM). International Journal of Pharmaceutics, 460(1-2), p.28–32.
Pratiwi, M. et al. (2015). Mammograms Classification Using Grey-level Co-occurrence Matrix and Radial Basis Function Neural Network. Procedia Computer Science, 59(Iccsci), p.83–91.
Qian, X., Hua, X., Chen, P., Ke, L. (2011). PLBP: An Effective Local Binary Patterns Texture Descriptor with pyramid representation. Pattern Recognition, 44, p.2502-2515.
Reddy, R.O.K., Reddy, B.E., Reddy, E.K. (2014). An Effective GLCM and Binary Pattern Schemes Based Classification for Rotation Invariant Fabric Textures. International Journal of Computer Engineering Science, 4(1), p.1-15.
Rivera, A.R., Castillo, R., Chae, O. (2013). Local directional number pattern for face analysis: Face and expression recognition, IEEE Trans. Image Process., 22(5), p.1740–1752.
Rivera, A.R., Chae, O. (2015). Spatiotemporal directional number transitional graph for dynamic texture recognition. IEEE Trans. Pattern Anal. Mach. Intell., 37(10), p.2146–2152.
Samaria, F., Harter, A. (1994). In Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision. Sarasota, Florida, U.S.
Shih, H.C. (2013). Robust gender classification using a precise patch histogram. Pattern Recognition, 46(2), p.519–528.
Staunton, R. (1999). Hexagonal Sampling in Image Processing. Advances in Imaging and Electro Physics, 107, p.231–307.
Stevenson, R.L., Arce, G.R. (1985). Binary display of hexagonally sampled continuous-tone images. Journal of the Optical Society of America A, 2, p.1009–1013.
Tan, X., Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process., 19(6), p.1635–1650.
Turk, M.A., Pentland, A.P. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), p.71-86.
Ulaby, F.T. et al. (1986). Textural Information in SAR Images. IEEE Transactions on Geoscience and Remote Sensing, GE-24(2), p.235–245.
Vujasinovic, T. et al. (2015). Grey-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk. Microscopy and Microanalysis, First View, p.1–9.
Watson, A.B., Ahumada, A.J. (1989). A hexagonal orthogonal-oriented pyramid as a model of image representation in the visual cortex. IEEE Transactions on Biomedical Engineering, BME-36, p.97–106.
Wenbo, W. et al. (2015). Sea Ice Classification of SAR Image Based on Wavelet Transform and GreyLevel Co-occurrence Matrix. In Proceedings of the 5th International Conference on Instrumentation and Measurement, Computer, Communication and Control (p.104–107). Qinhuangdao, China.
Yang, S., Bhanu, B. (2011). Facial expression recognition using emotion avatar image. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (p.866–871). Santa Barbara, CA, USA.
Yin, Q.B., Kim, J.N. (2008). Rotation-invariant texture classification using circular Gabor wavelets based local and global features. Chinese Journal of Electronics, 17(4), p.646–648.
Zhang, W.C., Shan, S.G., Gao, W., Zhang, H.M. (2005). Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In Proceedings of the 10th IEEE International Conference and Computer Vision (p. 786-791). Beijing, China.
Zhong, F., Zhang, J. (2013). Face Recognition with Enhanced Local Directional Patterns. Neurocomputing, 119, p.375-384.

Turkish Studies - Information Technologies and Applied Sciences-Cover

ISSN: 2667-5633
Yayın Aralığı: Yılda 4 Sayı
Başlangıç: 2006
Yayıncı: ASOS Eğitim Bilişim Danışmanlık Otomasyon Yayıncılık Reklam Sanayi ve Ticaret LTD ŞTİ