Skip to main content

Banknote recognition: investigating processing and cognition framework using competitive neural network

Cognitive Neurodynamics volume 11, pages 67–79 (2017)Cite this article

Abstract

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria’s Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  • Abas AR (2013) Adaptive competitive learning neural networks. Egypt Inform J 14(3):183–194

    Article  Google Scholar 

  • Abdipoor S, Nasseri A, Akbarpour M, Parsian H, Zamani S (2013) Integrating neural network and colonial competitive algorithm: a new approach for predicting bankruptcy in Tehran security exchange. Asian Econ Financ Rev 3(11):1528–1539

    Google Scholar 

  • Ahissar M, Hochstein S (2004) The reverse hierarchy theory of visual perceptual learning. Trends Cognit Sci 8(10):457–464

    Article  Google Scholar 

  • Amano K, Goda N, Nishida SY, Ejima Y, Takeda T, Ohtani Y (2006) Estimation of the timing of human visual perception from magnetoencephalography. J Neurosci 26(15):3981–3991

    CAS  Article  PubMed  Google Scholar 

  • Arslan CA (2013) Artificial neural network models investigation for euphrates river forecasting & back casting. J Asian Sci Res 3(11):1090–1104

    Google Scholar 

  • Axelrod V, Yovel G (2012) Hierarchical processing of face viewpoint in human visual cortex. J Neurosci 32(7):2442–2452

    CAS  Article  PubMed  Google Scholar 

  • Behrmann M, Geng JJ, Shomstein S (2004) Parietal cortex and attention. Curr Opin Neurobiol 14(2):212–217

    CAS  Article  PubMed  Google Scholar 

  • Biswas D (2011) Novel gray scale conversion techniques based on pixel depth. J Glob Res Comput Sci 2(6):118–121

    Google Scholar 

  • Castro GB, Martini JSC, Hirakawa AR (2014) Biologically-inspired neural network for traffic signal control. In: Proceedings of 2014 IEEE 17th international conference on intelligent transportation systems (ITSC), pp 2144–2149

  • Chaudhary V, Bhatia RS, Ahlawat AK (2014) A novel self-organizing map (SOM) learning algorithm with nearest and farthest neurons. Alex Eng J 53(4):827–831

    Article  Google Scholar 

  • Chaumon M, Drouet V, Tallon-Baudry C (2008) Unconscious associative memory affects visual processing before 100 ms. J Vis 8(3):1–10

    Article  PubMed  Google Scholar 

  • De Rover M, Petersson KM, Van der Werf SP, Cools AR, Berger HJ, Fernández G (2008) Neural correlates of strategic memory retrieval: differentiating between spatial-associative and temporal-associative strategies. Hum Brain Mapp 29(9):1068–1079

    Article  PubMed  Google Scholar 

  • Diamant E (2008) Unveiling the mystery of visual information processing in human brain. Brain Res 1225:171–178

    CAS  Article  PubMed  Google Scholar 

  • Eluyode OS, Akomolafe MB (2013) Comparative study of biological and artificial neural networks. Eur J Appl Eng Sci Res 2(1):36–46

    Google Scholar 

  • Fukai T, Tanaka S (1997) A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winners-share-all. Neural Comput 9(1):77–97

    CAS  Article  PubMed  Google Scholar 

  • Gilbert CD, Li W (2013) Top-down influences on visual processing. Nat Rev Neurosci 14(5):350–363

    CAS  Article  PubMed  Google Scholar 

  • Golosio B, Cangelosi A, Gamotina O, Masala GL (2015) A cognitive neural architecture able to learn and communicate through natural language. PLoS One 10(11):e0140866

    Article  PubMed  PubMed Central  Google Scholar 

  • Graboi D, Lisman J (2013) Recognition by top-down and bottom-up processing in cortex: the control of selective attention. J Neurophysiol 90(2):798–810

    Article  Google Scholar 

  • Hakimpoor H, Arshad KAB, Tat HH, Khani N, Rahmandoust M (2011) Artificial neural networks’ applications in management. World Appl Sci J 14(7):1008–1019

    Google Scholar 

  • Han X, Li Y (2015) The application of convolution neural networks in handwritten numeral recognition. Int J Database Theory Appl 8(3):367–376

    Article  Google Scholar 

  • Hasan S, Shamsuddin SM (2011) Multistrategy self-organizing map learning for classification problems. Comput Intell Neurosci 2011(1):1–11

    Article  Google Scholar 

  • Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR Upper Saddle River, NJ

    Google Scholar 

  • Herculano-Houzel S (2009) The human brain in numbers: a linearly scaled-up primate brain. Front Hum Neurosci 3(31):1–11

    Google Scholar 

  • Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 11:1254–1259

    Article  Google Scholar 

  • Khashman A (2006) Face recognition using neural networks and pattern averaging. In: Advances in neural networks-third international symposium on neural networks, Springer, Berlin, pp 98–103

  • Khashman A, Sekeroglu B (2004) Banknote identification using neural networks and image processing. In: Proceedings of the 2nd international electrical, electronics and computer engineering symposium (NEU-CEE’2004), pp 272–275

  • Khashman A, Sekeroglu B (2005) Multi-banknote identification using a single neural network. In: Proceedings of advanced concepts for intelligent vision systems, Springer, Berlin, pp 123–129

  • Khashman A, Sekeroglu B, Dimililer K (2005) Deformed banknote identification using pattern averaging and neural networks. In: Proceedings of the 4th WSEAS international conference on computational intelligence, ManMachine systems and cybernetics (CIMMACS’05), pp 233–237

  • Long LN, Gupta A (2008) Biologically-inspired spiking neural networks with Hebbian learning for vision processing. In: Proceedings of 46th AIAA aerospace sciences meeting, pp 2008–0885

  • McMains S, Kastner S (2011) Interactions of top-down and bottom-up mechanisms in human visual cortex. J Neurosci 31(2):587–597

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Milanova M, Rubin S, Kountchev R, Todorov V, Kountcheva R (2008) Combined visual attention model for video sequences. In: Proceedings of 19th IEEE international conference on pattern recognition, pp 1–4

  • Müller HJ, Krummenacher J (2006) Visual search and selective attention. Vis Cognit 14(4–8):389–410

    Article  Google Scholar 

  • Navalpakkam V, Itti L (2006) An integrated model of top-down and bottom-up attention for optimizing detection speed. In: Proceedings of 2006 IEEE computer society conference on computer vision and pattern recognition, pp 2049–2056

  • Niyogi P, Girosi F (1996) On the relationship between generalization error, hypothesis complexity, and sample complexity for radial basis functions. Neural Comput 8(4):819–842

    Article  Google Scholar 

  • Oliva A, Torralba A, Castelhano MS, Henderson JM (2003) Top-down control of visual attention in object detection. In: Proceedings of 2003 IEEE international conference on image processing 1, pp 1–253

  • Oyedotun OK, Khashman A (2016) Document segmentation using textural features summarization and feedforward neural network. Appl Intell 45(1):198–212

    Article  Google Scholar 

  • Pinto Y, van der Leij AR, Sligte IG, Lamme VA, Scholte HS (2013) Bottom-up and top-down attention are independent. J Vis 13(3):16

    Article  PubMed  Google Scholar 

  • Schuman CD, Birdwell JD (2013) Dynamic artificial neural networks with affective systems. PLoS One 8(11):e80455

    Article  PubMed  PubMed Central  Google Scholar 

  • Stafford R (2010) Constraints of biological neural networks and their consideration in AI applications. Adv Artif Intell 2010(1):1–6

    Article  Google Scholar 

  • Taylor JG, Alavi FN (1995) A global competitive neural network. Biol Cybern 72(3):233–248

    CAS  Article  PubMed  Google Scholar 

  • Voss JL (2009) Long-term associative memory capacity in man. Psychon Bull Rev 16(6):1076–1081

    Article  PubMed  Google Scholar 

  • Yamazaki T, Igarashi J (2013) Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit. Neural Netw 47:103–111

    Article  PubMed  Google Scholar 

  • Zhang Y, Zhao X, Fu H, Liang Z, Chi Z, Zhao X, Feng D (2011) A time delay neural network model for simulating eye gaze data. J Exp Theor Artif Intell 23(1):111–126

    Article  Google Scholar 

  • Zhang L, Xia GS, Wu T, Lin L, Tai XC (2015) Deep learning for remote sensing image understanding. J Sens 501(17369):1–2

    Google Scholar 

Download references

Author information

Affiliations

  1. European Centre for Research and Academic Affairs (ECRAA), Mersin-10, Northern Cyprus, Lefkosa, Turkey

    Oyebade K. Oyedotun & Adnan Khashman

  2. University of Kyrenia, Mersin-10, Girne, Turkey

    Adnan Khashman

Authors
  1. Oyebade K. Oyedotun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adnan Khashman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oyebade K. Oyedotun.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Oyedotun, O.K., Khashman, A. Banknote recognition: investigating processing and cognition framework using competitive neural network. Cogn Neurodyn 11, 67–79 (2017). https://doi.org/10.1007/s11571-016-9404-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-016-9404-2

Keywords

  • Banknote recognition
  • Competitive neural network
  • Neural processing
  • Cognition framework
  • Naira notes