Deep learning in vision-based static hand gesture recognition

Neural Computing and Applications - Tập 28 Số 12 - Trang 3941-3951 - 2017
Oyebade K. Oyedotun1, Adnan Khashman1
1European Centre for Research and Academic Affairs (ECRAA), Lefkosa, Mersin-10, Northern Cyprus, Turkey

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Tài liệu tham khảo

Nguyen T-N, Huynh H-H, Meunier J (2013) Static hand gesture recognition using artificial neural network. J Image Graph 1(1):34–38

Nagi J, Ducatelle F, Di Caro GA et al (2011) Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE international conference on signal and image processing applications (ICSIPA2011), pp 342–347

Rahman MdH, Afrin J (2013) Hand gesture recognition using multiclass support vector machine. Int J Comput Appl 74(1):39–43

Sultana A, Rajapuspha T (2012) Vision based gesture recognition for alphabetical hand gestures using the SVM classifier. Int J Comput Sci Eng Technol 3(7):218–223

Yewale SK, Bharne PK (2011) Hand gesture recognition using different algorithms based on artificial neural network. In: 2011 International conference on emerging trends in networks and computer communications (ETNCC), 22–24 April 2011, Udaipur, pp 287–292

Triesch J, von Malsburg C (2011) A system for person-independent hand posture recognition against complex backgrounds. IEEE Trans Pattern Anal Mach Intell 23(12):1449–1453

Oyedotun OK, Olaniyi EO, Helwan A, Khashman A (2014) Decision support models for iris nevus diagnosis considering potential malignancy. Int J Sci Eng Res 5(12):419–426

Ahmed T (2012) A neural network based real time hand gesture recognition system. Int J Comput Appl 59(4):17–22

Phu JJ, Tay YH (2006) Computer vision based hand gesture recognition using artificial neural network. Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, pp 1–6

Ibraheem NA, Khan RZ (2012) Vision based gesture recognition using neural networks approaches: a review. Int J Hum Comput Interact 3(1):1–14

Khashman A (2012) Investigation of different neural models for blood cell type identification. Neural Comput Appl 21(6):1177–1183

Khashman A (2009) Application of an emotional neural network to facial recognition. Neural Comput Appl 18(4):309–320

Oyedotun OK, Tackie SN, Olaniyi EO, Khashman A (2015) Data mining of students’ performance: Turkish students as a case study. Int J Intell Syst Appl 7(9):20–27

Wang W, Yang J, Xiao J et al (2015) Face recognition based on deep learning. Lect Notes Comput Sci 8944:812–820

Noda K, Yamaguchi Y, Nakadai K et al (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737

Collobert R, Weston J, Bottou L et al (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

Kruger N et al (2013) Deep hierarchies in the primate visual cortex: What can we learn for computer vision? IEEE Trans Pattern Anal Mach Intell 35(8):1847–1871

Thomas Moeslund’s gesture recognition database—PRIMA. http://www-prima.inrialpes.fr/FGnet/data/12-MoeslundGesture/database.html

Najafabadi MM et al (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1–21

Pierre B (2012) Autoencoders, unsupervised learning, and deep architectures. Workshop Unsuperv Transf Learn 27:37–50

Erhan D, Bengio Y, Courville A (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660

Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of 13th international conference on artificial intelligence and statistics, pp 249–256

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

Oyedotun OK, Dimililer K (2016) Pattern recognition: invariance learning in convolutional auto encoder network. Int J Image Graph Signal Process 8(3):19–27

LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

Oyedotun OK, Olaniyi EO, Khashman A (2015) Deep learning in character recognition considering pattern invariance constraints. Int J Intell Syst Appl 7(7):1–10

Vincent P et al (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014. Springer, Berlin, pp 818–833

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems. Neural Information Processing Systems (NIPS), pp 1097–1105

Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th international conference on machine learning (ICML-13), pp 1139–1147

Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras KI, Duch W, Iliadis LS (eds) Artificial neural networks—ICANN. Springer, Berlin, pp 92–101

Hasan H, Abdul-Kareem S (2014) Static hand gesture recognition using neural networks. Artif Intell Rev 41(2):147–181

Avraam M (2014) Static gesture recognition combining graph and appearance features. Int J Adv Res Artif Intell 3(2):1–4

Nguyen T-N, Huynh H-H, Meunier J (2015) Static hand gesture recognition using principal component analysis combined with artificial neural network. J Autom Control Eng 3(1):40–45