Skip to main content
SpringerLink
Log in

A new convolutional neural network based on a sparse convolutional layer for animal face detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper focuses on the face detection problem of three popular animal categories that need control such as horses, cats and dogs. Existing detectors are generally based on Convolutional Neural Networks (CNNs) as backbones. CNNs are strong and fascinating classification tools but present some weak points such as the big number of layers and parameters, require a huge dataset and ignore the relationship between image parts. To be precise, to deal with these problems, this paper contributes to present a new Convolutional Neural Network for Animal Face Detection (CNNAFD), a new backbone CNNAFD-MobileNetV2 for animal face detection and a new Tunisian Horse Detection Database (THDD). CNNAFD used a processed filters based on gradient features and applied with a new way. A new sparse convolutional layer ANOFS-Conv is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). The ANOFS method is used as a training optimizer for the new ANOFS-Conv layer. CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the new network CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. The proposed detector with the new CNNAFD-MobileNetV2 network provides effective results and proves to be competitive with the detectors of the related works with an Average Precision equal to 98.28%, 99.78%, 99.00% and 92.86% on the THDD, Cat Database, Stanford Dogs Dataset and Oxford-IIIT Pet Dataset respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

Notes

  1. https://ieee-dataport.org/open-access/thdd?fbclid=IwAR3usKGl8Mfltq8zgPTr_vft15qog5oOAEXYsDBe4IHcvBx11eUmnRRwG_I

  2. https://www.robots.ox.ac.uk/~vgg/data/pets/

  3. https://blog.roboflow.com/training-a-tensorflow-object-detection-model-with-a-custom-dataset/

References

  1. BenSaid F, Alimi AM (2016) A new online feature selection method for decision making problems with ultra-high dimension and massive training data. Journal of Information Assurance and Security, pp 293–301

  2. BenSaid F, Alimi AsM (2016) Ofsf-bc: Online feature selection framework for binary classification. In: International conference of computer systems and applications, pp 1–8

  3. BenSaid F, Alimi AM (2015) Anofs: Automated negotiation based online feature selection method. In: International conference on intelligent systems design and applications, pp 225–230

  4. BenSaid F, Alimi AM (2016) Multi agent-learner based online feature selection system. In: IEEE international conference on systems, man, and cybernetics (SMC), pp 003652–003657

  5. BenSaid F, Alimi AM (2021) Online feature selection system for big data classification based on multi-objective automated negotiation. Pattern Recogn 110:107–629

    Article  Google Scholar 

  6. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 886–893

  7. Duan J, Liao S, Zhou S, ZLi S (2016) Face classification: A specialized benchmark study. In: Chinese conference on biometric recognition, pp 22–29

  8. Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis (IJCV) 88:303–338

    Article  Google Scholar 

  9. Howse J, Alekhin A (2020) Haarcascade frontal cat face. https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalcatface.xml. Accessed 13 April 2020

  10. Howse J, Alekhin A (2020) Haarcascade frontal cat face extended. OpenCV, https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalcatface_extended.xml. Accessed 13 April 2020

  11. Huang G, Liu Z, Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2261–2269

  12. Jarraya I, Ouarda W, Alimi AM (2015) A preliminary investigation on horses recognition using facial texture features. In: IEEE International Conference on Systems, Man, and Cybernetics, pp 2803–2808

  13. Jarraya I, Ouarda W, Alimi AM (2017) Deep neural network features for horses identity recognition using multiview horses? face pattern. In: International conference on machine vision (ICMV), vol 10341, pp 52–56

  14. Jocher G, Stoken A, Borovec J et al (2020) ultralytics/yolov5: v3.0. https://zenodo.org/record/3983579#.YKUgT6HjLIU. Accessed 13 August 2020

  15. Joshi S, Verma DK, Saxena G, Paraye A (2019) Issues in training a convolutional neural network model for image classification. In: International conference on advances in computing and data sciences, pp 282–293

  16. Khosla A, Jayadevaprakash N, Yao B, Li F-F (2011) Novel dataset for fine-grained image categorization : Stanford dogs. In: First workshop on fine-grained visual categorization, CVPR

  17. Kishore T, Jha A, Kumar S, Bhattacharya S, Sultana M (2021) Deep cnn based automatic detection and identification of bengal tigers. In: Computational intelligence in communications and business analytics, pp 503–507

  18. Korte T (2014) 10,000 cat pictures (for science). Center For Data Innovation. https://datainnovation.org/2014/08/10000-cat-pictures-for-science/. Accessed 21 August 2014

  19. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. European Conference on Computer Vision 9905:21–37

    Google Scholar 

  20. Liu W, Hasan I, Liao S (2020) Center and scale prediction: A box-free approach for pedestrian and face detection. In: arXiv:1904.02948v3

  21. Manoj S, Rakshith S, Kanchana V (2021) Identification of cattle breed using the convolutional neural network. In: International conference on signal processing and communication. https://doi.org/10.1109/ICSPC51351.2021.9451706, pp 503–507

  22. Mukai N, Zhang Y, Chang Y (2018) Pet face detection. In: Nicograph International (NicoInt). https://doi.org/10.1109/NICOINT.2018.00018, pp 52–57

  23. Nelson J (2020) Training a tensorflow mobilenet object detection model with a custom dataset. Roboflow. https://blog.roboflow.com/training-a-tensorflow-object-detection-model-with-a-custom-dataset/. Accessed 09 February 2020

  24. Ouarda W, Trichili H, Alimi AM, Solaiman B (2013) Combined local features selection for face recognition based on na?ve bayesian classification. In: International conference on hybrid intelligent systems, pp 240–245

  25. Ouarda W, Trichili H, Alimi AM, Solaiman B (2014) Face recognition based on geometric features using support vector machines. In: International conference of soft computing and pattern recognition, pp 89–95

  26. Ouarda W, Trichili H, Alimi AM, Solaiman B (2014) Mlp neural network for face recognition based on gabor features and dimensionality reduction techniques. In: International conference on multimedia computing and systems. https://doi.org/10.1109/ICMCS.2014.6911265, pp 127–134

  27. Ouarda W, Trichili H, Alimi AM, Solaiman B (2015) Bag of face recognition systems based on holistic approaches. In: International conference on intelligent systems design and applications (ISDA), pp 201–206

  28. Ouarda W, Trichili H, Alimi A M, Solaiman B (2016) Towards a novel biometric system for smart riding club. Journal of information assurance and security (JIAS) 11:201–213

    Google Scholar 

  29. Parkhi OM, Vedaldi A, Zisserman A, Jawahar CV (2012) Cats and dogs. In: IEEE conference on computer vision and pattern recognition

  30. Paul V, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57:137?154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb

    Google Scholar 

  31. Rathor S, Kumari S, Singh R, Gupta P (2021) Two layers machine learning architecture for animal classification using hog and lbp. In: Proceedings of international conference on communication and artificial intelligence, pp 445–453

  32. Redmon J, Farhadi A (2016) Yolo9000: Better, faster, stronger. arXiv:1612.08242

  33. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767v1

  34. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, vol 28, pp 91–99

  35. Sabour S, Frosst N, Hinton G (2017) Dynamic routing between capsules. In: Proceedings of the 31st international conference on neural information processing systems, p 3859?3869

  36. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: IEEE conference on computer vision and pattern recognition, pp 4510–4520

  37. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations

  38. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2818–2826

  39. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2818–2826

  40. Taunk P, Jayasri G, Priya J P, Kumar NS (2020) Face detection using viola jones with haar cascade. Test Engineering and Management, 83

  41. Tureckova A, Holik T, Oplatkova ZK (2020) Dog face detection using yolo network. MENDEL Soft Computing Journal 26:2571–3701

    Google Scholar 

  42. Vlachynska A, Oplatkova ZK, Turecek T (2019) Dog face detection and localization of dogface’s landmarks. Advances in Intelligent Systems and Computing 764:465–476

    Article  Google Scholar 

  43. Wang C-Y, Liao H-YM, Yeh I-H, Wu Y-H, Chen P-Y, Hsieh J-W (2019) Cspnet: A new backbone that can enhance learning capability of cnn. In: ArXiv: Comput Vis Pattern Recognit

  44. Wu Y, Kirillov A, Massa F, Lo W-Y, Girshick R (2019) Detectron2. https://github.com/facebookresearch/detectron2

  45. Xie S, Girshick R, Dollár P, Tu Z, He K (2016) Aggregated residual transformations for deep neural networks. arXiv:1611.05431

  46. Yamada A, Kojima K, Kiyama J, Okamoto M, Murata H (2011) Directional edge-based dog and cat face detection method for digital camera. In: IEEE international conference on consumer electronics (ICCE), pp 87–88

  47. Zagoruyko S, Komodakis N (2017) Wide residual networks. arXiv:1605.07146v4

  48. Zhang B, Li J, Wang Y, Tai Y, Wang C, Li J, Huang F, Xia Y, Pei W, Ji R (2020) Asfd: Automatic and scalable face detector. arXiv:2003.11228

  49. Zhang C, Zhang Z (2010) A survey of recent advances in face detection. Microsoft, https://www.microsoft.com/en-us/research/publication/a-survey-of-recent-advances-in-face-detection/. Accessed June 2010

  50. Zhang W, Sun J, Tang X (2011) From tiger to panda: Animal head detection. IEEE Trans Image Process 20:1696–1708

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgment

The authors would like to acknowledge that the THDD was created with the help of four Riding Clubs in Sfax, Tunisia: Equestrian Clubs of road Mahdia, road Tunis in Sakiet Ezzit, road Ain Km 17, and road Saltnia Km 17.

Funding

The research leading to these results has received funding from the Tunisian Ministry of Higher Education and Scientific Research under the grant agreement number LR11ES48.

Author information

Authors and Affiliations

  1. REGIM-Lab.: REsearch Groups in Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia

    Islem Jarraya, Fatma BenSaid, Wael Ouarda & Adel M. Alimi

  2. Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax, 3021, Tunisia

    Wael Ouarda

  3. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata, 700 108, India

    Umapada Pal

  4. Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Adel M. Alimi

Authors
  1. Islem Jarraya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatma BenSaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wael Ouarda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Umapada Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adel M. Alimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Islem Jarraya.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Financial interests

The authors declare they have no financial interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jarraya, I., BenSaid, F., Ouarda, W. et al. A new convolutional neural network based on a sparse convolutional layer for animal face detection. Multimed Tools Appl 82, 91–124 (2023). https://doi.org/10.1007/s11042-022-12610-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12610-y

Keywords

Search

Navigation