nach oben

Neural Computing and Applications

Erschienen in:

15.09.2020 | Original Article

Convolutional neural network with spatial pyramid pooling for hand gesture recognition

verfasst von: Yong Soon Tan, Kian Ming Lim, Connie Tee, Chin Poo Lee, Cheng Yaw Low

Erschienen in: Neural Computing and Applications | Ausgabe 10/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Hand gesture provides a means for human to interact through a series of gestures. While hand gesture plays a significant role in human–computer interaction, it also breaks down the communication barrier and simplifies communication process between the general public and the hearing-impaired community. This paper outlines a convolutional neural network (CNN) integrated with spatial pyramid pooling (SPP), dubbed CNN–SPP, for vision-based hand gesture recognition. SPP is discerned mitigating the problem found in conventional pooling by having multi-level pooling stacked together to extend the features being fed into a fully connected layer. Provided with inputs of varying sizes, SPP also yields a fixed-length feature representation. Extensive experiments have been conducted to scrutinize the CNN–SPP performance on two well-known American sign language (ASL) datasets and one NUS hand gesture dataset. Our empirical results disclose that CNN–SPP prevails over other deep learning-driven instances.

Vorheriger Artikel Prescribed-time convergent and noise-tolerant Z-type neural dynamics for calculating time-dependent quadratic programming

Nächster Artikel Identifying crop water stress using deep learning models

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Lim KM, Tan AW, Tan SC (2016) Block-based histogram of optical flow for isolated sign language recognition. J Vis Commun Image Represent 40:538–545CrossRef

Lim KM, Tan AW, Tan SC (2016) A feature covariance matrix with serial particle filter for isolated sign language recognition. Expert Syst Appl 54:208–218CrossRef

Lim KM, Tan AW, Tan SC (2017) A four dukkha state-space model for hand tracking. Neurocomputing 267:311–319CrossRef

Kour KP, Mathew L (2017) Sign language recognition using image processing. Int J Adv Res Comput Sci Softw Eng 7(8):10CrossRef

Kumar BP, Manjunatha M (2017) A hybrid gesture recognition method for American sign language. Indian J Sci Technol 10(1):1–12CrossRef

He Y, Li G, Liao Y, Sun Y, Kong J, Jiang G, Jiang D, Tao B, Xu S, Liu H (2017) Gesture recognition based on an improved local sparse representation classification algorithm. Cluster Comput 22:10935–10946CrossRef

Muthukumar K, Poorani S, Gobhinath S (2017) Vision based hand gesture recognition for Indian sign languages using local binary patterns with support vector machine classifier. Adv Natl Appl Sci 11(6):314–322

Hu Y (2018) Finger spelling recognition using depth information and support vector machine. Multimedia Tools Appl 77(21):29043–29057CrossRef

Pariwat T, Seresangtakul, P (2017) Thai finger-spelling sign language recognition using global and local features with SVM. In: 2017 9th international conference on knowledge and smart technology (KST), pp 116–120. IEEE

10.

Silanon K (2017) Thai finger-spelling recognition using a cascaded classifier based on histogram of orientation gradient features. Computational intelligence and neuroscience 2017

11.

Jadooki S, Mohamad D, Saba T, Almazyad AS, Rehman A (2017) Fused features mining for depth-based hand gesture recognition to classify blind human communication. Neural Comput Appl 28(11):3285–3294CrossRef

12.

Zare AA, Zahiri SH (2018) Recognition of a real-time signer-independent static Farsi sign language based on fourier coefficients amplitude. Int J Mach Learn Cybernet 9(5):727–741CrossRef

13.

Nai W, Liu Y, Rempel D, Wang Y (2017) Fast hand posture classification using depth features extracted from random line segments. Pattern Recogn 65:1–10CrossRef

14.

Hu Y, Zhao HF, Wang ZG (2018) Sign language fingerspelling recognition using depth information and deep belief networks. Int J Pattern Recognit Artif Intell 32(06):1850018MathSciNetCrossRef

15.

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

16.

Lim KM, Tan AWC, Lee CP, Tan SC (2019) Isolated sign language recognition using convolutional neural network hand modelling and hand energy image. Multimedia Tools Appl 78:1–28CrossRef

17.

Nakjai P, Katanyukul T (2019) Hand sign recognition for Thai finger spelling: an application of convolution neural network. J Signal Process Syst 91(2):131–146CrossRef

18.

Li Y, Wang X, Liu W, Feng B (2018) Deep attention network for joint hand gesture localization and recognition using static rgb-d images. Inf Sci 441:66–78MathSciNetCrossRef

19.

Kwolek B, Sako S (2017) Learning siamese features for finger spelling recognition. In: International conference on advanced concepts for intelligent vision systems, Springer, pp 225–236

20.

Hosoe H, Sako S, Kwolek B (2017) Recognition of jsl finger spelling using convolutional neural networks. In: 2017 Fifteenth IAPR international conference on machine vision applications (MVA), IEEE, pp 85–88

21.

Gao Q, Liu J, Ju Z, Li Y, Zhang T, Zhang L (2017) Static hand gesture recognition with parallel cnns for space human-robot interaction. In: International conference on intelligent robotics and applications, Springer, pp 462–473

22.

Kania, K, Markowska-Kaczmar U (2018) American sign language fingerspelling recognition using wide residual networks. In: International conference on artificial intelligence and soft computing, Springer, pp 97–107

23.

Oliveira M, Chatbri H, Little S, Ferstl Y, O’Connor NE, Sutherland A (2017) Irish sign language recognition using principal component analysis and convolutional neural networks. In: 2017 international conference on digital image computing: techniques and applications (DICTA), IEEE, pp 1–8

24.

Flores CJL, Cutipa AG, Enciso RL (2017) Application of convolutional neural networks for static hand gestures recognition under different invariant features. In: 2017 IEEE XXIV international conference on electronics, electrical engineering and computing (INTERCON), IEEE, pp 1–4

25.

Alani AA, Cosma G, Taherkhani A, McGinnity T (2018) Hand gesture recognition using an adapted convolutional neural network with data augmentation. In: 2018 4th international conference on information management (ICIM), IEEE, pp 5–12

26.

Arenas JOP, Moreno RJ, Beleño RDH (2018) Convolutional neural network with a dag architecture for control of a robotic arm by means of hand gestures. Contemp Eng Sci 11(12):547–557CrossRef

27.

Tazhigaliyeva N, Kalidolda N, Imashev A, Islam S, Aitpayev K, Parisi GI, Sandygulova A (2017) Cyrillic manual alphabet recognition in rgb and rgb-d data for sign language interpreting robotic system (slirs). In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 4531–4536

28.

Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Bach F, Blei D (eds) Proceedings of the 32nd international conference on machine learning, Proceedings of machine learning research, vol 37, pp 448–456. PMLR, Lille, France

29.

He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916CrossRef

30.

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):1929–1958MathSciNetMATH

31.

Pugeault N, Bowden R (2011) Spelling it out: real-time asl fingerspelling recognition. In: 2011 IEEE international conference on computer vision workshops (ICCV workshops), IEEE, pp 1114–1119

32.

Barczak ALC, Reyes N.H, Abastillas M, Piccio A, Susnjak T (2011) A new 2d static hand gesture colour image dataset for asl gestures

33.

Kumar PP, Vadakkepat P, Loh AP (2010) Hand posture and face recognition using a fuzzy-rough approach. Int J Humanoid Rob 7(03):331–356CrossRef

34.

Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298CrossRef

35.

Zhang L, Yang F, Zhang YD, Zhu YJ (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE international conference on image processing (ICIP), IEEE, pp 3708–3712

36.

Kagaya H, Aizawa K, Ogawa, M (2014) Food detection and recognition using convolutional neural network. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 1085–1088

37.

Pigou L, Dieleman S, Kindermans PJ, Schrauwen B (2014) Sign language recognition using convolutional neural networks. In: European conference on computer vision, Springer, pp 572–578

38.

Ma Y, Zhou G, Wang S, Zhao H, Jung W (2018) SignFi: sign language recognition using WiFi. Proc ACM Interact Mobile Wearable Ubiquit Technol 2(1):1–21CrossRef

Titel: Convolutional neural network with spatial pyramid pooling for hand gesture recognition
verfasst von: Yong Soon Tan
Kian Ming Lim
Connie Tee
Chin Poo Lee
Cheng Yaw Low
Publikationsdatum: 15.09.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05337-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 10/2021

Two-branch encoding and iterative attention decoding network for semantic segmentation

Copy-move forgery detection technique based on discrete cosine transform blocks features

Multi-source data fusion for economic data analysis

Research on trend analysis method of multi-series economic data based on correlation enhancement of deep learning

Large-area damage image restoration algorithm based on generative adversarial network

ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting