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Real-time face detection using circular sliding of the Gabor energy and neural networks

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Abstract

Face detection is one of the most important subjects in image processing. Over time, researchers have paid much attention to the subject, and they have made tremendous progress in the quality of face detection. In addition to the quality of face detection, the speed of face detection is of prime importance. In this paper, a real-time approach is presented for face detection using the Gabor filters and the neural networks that can be implemented in IoT devices. The Gabor filters are one of the most powerful tools in image processing, but they are rarely used in real-time applications due to high computational complexity. To overcome the problem, a new algorithm is proposed for processing images and detecting faces called circular sliding window (CSW). This new algorithm can reduce the number of sub-images generated by almost 98% related to the sliding window algorithm, in frontal face images which have symmetry. Also, a new Gabor feature called compressed Gabor feature (CGF) is employed which improves the speed of classification due to reducing the size of feature vector of the neural network. In the proposed method, the best speed of face detection and the worst speed of face detection for faces with size of 64 × 64 pixels are 0.0072 and 0.0092 s, respectively. The sensitivity of face detection in the proposed method is 95%, approximately.

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References

  1. Tsaia, Y.-H., Lee, Y.-C., Ding, J.-J., Chang, R.Y., Hsu, M.-C.: Robust in-plane and out-of-plane face detection algorithm using frontal face detector and symmetry extension. Image Vis. Comput. 78, 26–41 (2018)

    Article  Google Scholar 

  2. He, Y., Xu, D., Wu, L., Jian, M., Xiang, S., Pan, C.: LFFD: A light and fast face detector for edge devices. http://arxiv.org/abs/1904.10633v3(2019)

  3. Tang, X., Du, D. K., He, Z., Liu, J.: Pyramidbox: A context-assisted single shot face detector. In: European Conference on Computer Vision, pp. 812–828 (2018).

  4. Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Huang, F. :Dsfd: dual shot face detector. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5055–5064 (2019).

  5. Chahla, C., Snoussi, H., Abdallah, F., Dornaika, F.: Learned versus handcrafted features for person Re-identification. Int. J. Pattern Recognit Artif Intell. 34, 1–19 (2020)

    Article  Google Scholar 

  6. Xu, Y., Yan, W., Yang, G., Luo, J., Li, T., He, J.: CenterFace: joint face detection and alignment using face as point. Sci. Prog. 2020, 1–8 (2020)

    Google Scholar 

  7. Kang, S., Choi, B., Jo, D.: Faces detection method based on skin color modeling. J. Syst. Architect. 64, 100–109 (2016)

    Article  Google Scholar 

  8. Chihaoui, M., Elkefi, A., Bellil, W., Chokri Ben, A.: A survey of 2D face recognition techniques. Computers 5, 1–28 (2016)

    Article  Google Scholar 

  9. Jones, M.J., Viola, P.: Robust real-time face detection. Int. J. Comput. Vision 57, 137–154 (2004)

    Article  Google Scholar 

  10. Zhang, S., Wang, X., Lei, Z., Li, S.Z.: FaceBoxes: A CPU real-time and accurate unconstrained face detector. Neurocomputing 364, 297–309 (2019)

    Article  Google Scholar 

  11. Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: YOLO-face: a real-time face detector. Vis. Comput. 37, 805–813 (2021)

    Article  Google Scholar 

  12. Rehman, B., Ong, W.H., Tan, A.C.H., Ngo, T.D.: Face detection and tracking using hybrid margin-based ROI techniques. Vis. Comput. 36, 633–647 (2020)

    Article  Google Scholar 

  13. Singh, R., Goel, A., Raghuvanshi, D.K.: Computer-aided diagnostic network for brain tumor classification employing modulated Gabor filter banks. Vis. Comput. 36, 1–20 (2020)

    Article  Google Scholar 

  14. Mohammadian Fini, R., Mahlouji, M., Shahidinejad, A.: Multi-view face detection in open environments using Gabor features and neural networks. J. AI Data Mining 8, 461–470 (2020)

    Google Scholar 

  15. Francesco, C., Sander, S., Twan, B., Henk, C. :RASW: A run-time adaptive sliding window to improve Viola-Jones object detection. In: Seventh International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6 (2013).

  16. Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques (3rd ed.). Morgan Kaufmann.

  17. Yang, S., Luo, P., Loy, C. C., Tang, X.: WIDER FACE: A face detection benchmark. In :IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pp. 5525–5533 (2016).

  18. Markus Weber image databse. http://www.vision.caltech.edu/Image_Datasets/faces/faces.tar (1999).

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Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Qom branch, Qom, Iran

    Reza Mohammadian Fini & Ali Shahidinejad

  2. Department of Telecommunications, Islamic Azad University, Kashan branch, Kashan, Iran

    Mahmoud Mahlouji

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  1. Reza Mohammadian Fini
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  2. Mahmoud Mahlouji
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  3. Ali Shahidinejad
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Correspondence to Mahmoud Mahlouji.

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Mohammadian Fini, R., Mahlouji, M. & Shahidinejad, A. Real-time face detection using circular sliding of the Gabor energy and neural networks. SIViP 16, 1081–1089 (2022). https://doi.org/10.1007/s11760-021-02057-3

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  • DOI: https://doi.org/10.1007/s11760-021-02057-3

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