Mechanisms for increasing the efficiency of holistic face recognition systems

• Autorzy: Nowosielski A.

Warianty tytułu

PL

Mechanizmy poprawy skuteczności holistycznych systemów rozpoznawania twarzy

• Języki publikacji: EN

Abstrakty

EN

In holistic face recognition systems face is represented by set of features derived directly from brightness of 2D image pixels. To ensure proper representation of face under variability in head rotation, lighting and expression many templates should be provided. Mechanisms for increasing the efficiency of holistic face recognition systems are considered. By expanding the base structure with additional function blocks, without incorporating intricate feature extraction methods, it is possible to increase the effectiveness of the system.

PL

W systemach rozpoznawania twarzy opartych na cechach holistycznych (globalnych) twarz jest reprezentowana poprzez zbiór cech wyliczanych bezpośredno z jasności pikseli obrazu 2D. Takie rozwiązanie wymaga odpowiedniej liczby wzorców, aby zapewnić właściwą reprezentatywność twarzy przy obrotach głowy, zmiennym oświetleniu i różnorodności wyrazów twarzy. Mechanizmy zwiększania skuteczności rozpoznawania w tego typu systemach są rozważane w artykule. Poprzez rozszerzenie bazowej struktury za pomocą dodatkowych bloków funkcyjnych, bez wprowadzania skomplikowanych metod ekstrakcji cech, możliwa jest poprawa skuteczności działania systemu.

Słowa kluczowe

EN

face recognition; feature extraction; holistic face recognition system

PL

rozpoznawanie twarzy; ekstrakcja cech; holistyczne rozpoznawanie twarzy

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2015
• Tom: R. 91, nr 2
• Strony: 51--55
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Nowosielski A.

• Zachodniopomorski Uniwersytet Technologiczny, Wydział Informatyki, ul. Żołnierska 52, 71-210 Szczecin

Bibliografia

• [1] Kukharev G., Kuźmiński A., Biometric Techniques Part I. Face Recognition Methods (in Polish), Szczecin University of Technology Press, (2003)
• [2] Mikłasz M., Olszewski P., Nowosielski A., Kawka G., Pedestrian Traffic Distribution Analysis Using Face Recognition Technology, TST 2013, CCIS, 395 (2013), 303-312
• [3] Park U., Jain A.K., Face Matching and Retrieval Using Soft Biometrics, IEEE Transactions on Information Forensics and Security, 5 (2010), No. 3, 406-415
• [4] Kukharev G., Buda K., Shchegoleva N.L., Methods of face photo-sketch comparison, Pattern Recognition and Image Analysis,24 (2014), No. 1, 102-113
• [5] Kukharev G., Kuzminski A., Nowosielski A., Structure and Characteristics of Face Recognition Systems, Computing, Multimedia and Intelligent Techniques. Special issue on Live Biometrics and Security, 1 (2005), No. 1, 111-124
• [6] Smiatacz M., Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces – How to Face the Face Verification Task, Advances in Intelligent Systems and Computing, 226 (2013), 187-196, 2013
• [7] Kukharev G., Forczmański P., Nowosielski A., Simple facial features extractors utilization in hardware-software biometric systems (in Polish), Pomiary Automatyka Kontrola, 7-bis2006 (2006), 77-79
• [8] Brunelli R., Poggio T., Face Recognition: Features versus Templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (1993), No. 10, 1042-1052
• [9] Kong S.G., Heo J., Abidi B.R., Paik J., Abidi A.M., Recent advances in visual and infrared face recognition - a review, Computer Vision and Image Understanding, 97 (2005), No. 1, 103-135
• [10] Kong S.G., Heo J., Boughorbel F., Zheng Y., Abidi B.R., Koschan A., Yi M., Abidi M.A., Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition, International Journal of Computer Vision,71 (2007), No. 2, 215-233
• [11] Nowosielski A., Face Recognition Using DCT and LDA, Computer Recognition Systems, Advances in Soft Computing, 30 (2005), 799-806
• [12] Bowyer K.W., Chang K., Flynn P., A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition, Computer Vision and Image Understanding,101(2006), No. 1, 1–15
• [13] Kamencay P., Hudec R., Benco M., Zachariasova M., 2D-3D Face Recognition Method Based on a Modified CCAPCA Algorithm, Int J Adv Robot Syst, 2014, 11:36. doi: 10.5772/58251
• [14] Fabry T., Smeets D., Vandermeulen D., Surface representations for 3D face recognition, Face Recognition,(2010), 273-294
• [15] Liau H.F., Seng K.P., Ang L.-M., Chin S.W., New Parallel Models for Face Recognition, Recent Advances in Face Recognition, (2008), 15-26
• [16] Nowosielski A., Automatic template Selection for the Face Recognition Systems (in Polish), Metody Informatyki Stosowanej w Technice i Technologii, 8 (2005), 467-476
• [17] The ORL Database of Faces, http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
• [18] Hua G., Face Recognition by Discriminative Orthogonal Rankone Tensor Decomposition, Recent Advances in Face Recognition, (2008), 39-54
• [19] Guha T., Jonathan Wu Q.M., Curvelet Based Feature Extraction, Face Recognition, (2010), 35-45
• [20] Daleno D., Cariello L., Giannini M., Mastronardi G., Pseudo 2D Hidden Markov Model and Neural Network Coefficients in Face Recognition, Face Recognition, (2010), 151-170
• [21] Guo Z., Wang H., Liu Q., (2D)²LDALPP: A Novel Approach to Face Recognition, International Journal of Advanced Robotic Systems, (2012), 9:221. doi: 10.5772/53786
• [22] Saradha A., Annadurai S., A Hybrid Feature Extraction Approach for Face Recognition Systems, ICGST International Journal on Graphics, Vision and Image Processing, 5 (2005), No. 5, 23-30
• [23] Eickeler S., Mül ler S., Rigoll G., Recognition of JPEG compressed face images based on statistical methods, Image and Vision Computing, 18 (2000), No. 4, 279-287
• [24] Haddadnia J., Ahmadi M., N-feature neural network human face recognition, Image and Vision Computing, 22(2004), No. 12, 1071-1082