Abstract
To capture a stable picture through the digital camera is a challenging task in computer vision. The While identifying facial features such as misalignment, a parasitic light effect and a change in object position are the errors found in image processing. These errors get aggregated with images and cumulatively create distortion in the output video, which makes facial feature recognition more complicated in the video. In this paper, a solutions for unconstrained facial detection from digital image processing has been proposed, which fulfilled two requirements; first is a reliable method to extract the facial feature of the humans from a video and second is the estimation of 3D-image of human from the motion video. To meet these requirements, we develop a hybrid estimation method that combines the feature selection and extraction of facial features of the human from the video. Here we have extended the estimation of 2D to 3D unconstrained facial feature recognition. In the results, we found that the object in images is detected and we are able to develop the 3D sketch of human from the video. Further to validate the robustness of the proposed method, we have performed comprehensive testing on the huge dataset. The output of testing shows that the proposed method would be better to identify multiple facial features.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110.
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient based learning applied to document recognition. Proceedings of IEEE, 86(11), 2278–2324.
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E., & Benosman, R. B. (2017). HOTS: A hierarchy of event-based time-surfaces for pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(7), 1346–1359.
Mead, C., & Mahowald, M. (1991). The silicon retina. Science America, 264, 76–82.
Boahen, K. A. (2000). Point-to-point connectivity between neuromorphic chips using address-events. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 47(5), 416–434.
Kafai, M., An, L., & Bhanu, B. (2014). Reference face graph for face recognition. IEEE Transactions on Information Forensics and Security, 9(12), 2132–2143.
Punnappurath, A., Rajagopalan, A. N., Taheri, S., Chellappa, R., & Seetharaman, G. (2015). Face recognition across non-uniform motion blur, illumination, and pose. IEEE Transactions on Image Processing, 24(7), 2067–2082.
An, L., Kafai, M., Yang, S., & Bhanu, B. (2016). Person reidentification with reference descriptor. IEEE Transaction on Circuits and System for Video Technology, 26(4), 776–787.
Tyagi, R., Tomar, G. S., & Shirvastava, L. (2016). Unconstrained face recognition quality: A review. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(11), 199–210.
Tyagi, R., Tomar, G. S., & Baik, N. (2016). A survey of unconstrained face recogition algorithms and its application. International Journal of Security and its Application, 10(12), 369–376.
Tyagi, R., & Tomar, G. S. (2016). Tranformation of image from color to gray scale using contrast among DPCM and LMS method. Internation Journal of Signal Processing, Image Processing and Pattern Recognition, 9(8), 11–24.
Tyagi, R., & Tomar, G. S. (2016). Unfamiliar sides, video, image enhancement in face recognition. International Journal of Hybrid Information Technology, 9(11), 255–266.
Yuan, X., Tang, D., Liu, Y., Ling, Q., & Fang, L. (2017). Magic glasses: From 2D to 3D. IEEE Transactions on Circuits and Systems for Video Technology, 27(4), 843–854.
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., & Ho, A. T. S. (2015). Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security, 10(4), 762–777.
Hsu, S. B., Han, C. C., Wen, M. G., Wu, Y. C., & Fan, K. C. (2016). Extraction of visual facial features for health management. IEEE Systems Journal, 10(3), 992–1002.
Kamarol, S. K. A., Jaward, M. H., Parkkinen, J., & Parthiban, R. (2016). Spatiotemporal feature extraction for facial expression recognition. IET Image Processing, 10(7), 534–541.
Jung, J. Y., Kim, S. W., Yoo, C. H., Park, W. J., & Ko, S. J. (2016). LBP-ferns-based feature extraction for robust facial recognition. IEEE Transactions on Consumer Electronics, 62(4), 446–453.
Nguyen, H. T., & Caplier, A. (2015). Local patterns of gradients for face recognition. IEEE Transactions on Information Forensics and Security, 10(8), 1739–1751.
Tordoff, B., Murray, D. W.: Guided Sampling and Consensus for Motion Estimation. In European Conference on Computer Vision ECCV 2002 Lecture Notes in Computer Science book series (LNCS), 2350 (pp. 82–96).
Jeon, S., Yoon, I., Jang, J., Yang, S., Kim, J., & Paik, J. (2017). Robust video stabilization using particle keypoint update and l1-optimized camera path. MDPI Sensors, 2017, 1–18.
Litvin, A., Konrad, J., & Karl, C. W. (2003). Probabilistic video stabilization using Kalman filtering and mosaicking. Image and Video Communication and Processing, 2003, 663–674.
Alahi, A., Ortiz, R., Vandergheynst, P. (2012). FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, Providence, USA, June 2012 (pp.16–21).
"Mathworks," MATLAB, 25 Dec 2017. https://www.mathworks.com/products/computer-vision.html.
Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: paradigm for model fitting with applications to image analysis and automated cartography. Magazine Communications of the ACM, 24(6), 381–395.
Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (2011). Computer Aided Systems Theory—EUROCAST 2011, LNCS6928 Springer, 2011.
Kanatani, K., Sugaya, Y., & Kanazawa, Y. (2016). Ellipse fitting for computer vision: Implementation and applications. Morgan and Claypool Publishers, 6(1), 1–141.
"https://in.mathworks.com/help/vision/ref/disparity. html?requestedDomain=true," MATLAB, https://in.mathworks.com/help/vision/ref/disparity.html?requestedDomain=true. Retrieved 01 01 2018
Viola, P. A., Jones, M. J. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE CVPR, 2001.
Bradski, G. R. (1998). Real Time Face and Object Tracking as a Component of a Perceptual User Interface. In Proceedings of the 4th IEEE Workshop on Applications of Computer Vision, 1998.
Torr, P. H. S., & Zisserman, A. (2000). MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1), 138–156.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.
https://drive.google.com/open?id=10Wh1GwFFvI34-I4XLvP8ceT0ZukI-lxg.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1
Appendix 1
Comparison Table with different Data-set.
Sample of dataset | Video length (second) | Dimension | Size (MB) | Data rate (Mbit/sec) | Frame per second (FPS) | Number of person identified | Acquisition distance of object from camera (m) |
|---|---|---|---|---|---|---|---|
First | 13 | 720 × 480 | 5.7 | 3.60 | 30.26 | 1 | 0.8 |
Second | 48 | 480 × 480 | 1.8 | 0.29825 | 29.97 | 2 | 1.0 |
Third | 15 | 1428 × 803 | 3.2 | 17.66 | 29.97 | 2 | 1.5 |
Fourth | 26 | 720 × 480 | 10.4 | 3.2 | 25.91 | 1 | 0.5 |
Fifth | 09 | 720 × 480 | 4.1 | 3.61 | 30.28 | 2 | 1.0 |
Sixth | 10 | 720 × 480 | 4 | 3.27 | 26.67 | 6 | 2.0 |
Seventh | 09 | 1920 × 1080 | 23.3 | 18.68 | 29 | 1 | 2.4 |
Eighth | 07 | 1920 × 1080 | 17.7 | 20.24 | 30 | 1 | 2.0 |
Ninth | 08 | 1920 × 1080 | 19.6 | 19.59 | 29 | 1 | 1.5 |
Tenth | 09 | 1920 × 1080 | 23.1 | 18.54 | 30 | 1 | 3.0 |
Eleventh | 09 | 1920 × 1080 | 22.5 | 20.09 | 30 | 1 | 3.5 |
Twelve | 06 | 1920 × 1080 | 16.8 | 19.18 | 29 | 1 | 1.0 |
Thirteen | 09 | 1920 × 1080 | 23.8 | 19.16 | 29 | 1 | 2.0 |
Fourteen | 09 | 1920 × 1080 | 22.7 | 20.23 | 30 | 1 | 4.0 |
Fifteen | 09 | 1920 × 1080 | 23.8 | 19.12 | 29 | 1 | 1.0 |
Sixteen | 10 | 1920 × 1080 | 24.2 | 19.49 | 30 | 4 | 2.0 |
Seventeen | 09 | 1920 × 1080 | 23.9 | 19.17 | 30 | 4 | 1.0 |
Eighteen | 04 | 1920 × 1080 | 12.9 | 19.24 | 29 | 2 | 2.0 |
Nineteen | 14 | 1920 × 1080 | 24.5 | 13.29 | 29 | 1 | 0.4 |
Twenty | 08 | 1920 × 1080 | 20.0 | 18.13 | 29 | 2 | 1.4 |
Twenty one | 26 | 176 × 144 | 0.79 | 0.15625 | 8 | 1 | 0.2 |
Twenty two | 45 | 854 × 480 | 3.74 | 0.55175 | 25 | 1 | 0.5 |
Twenty three | 14 | 1920 × 1080 | 30.4 | 16.38 | 29 | 1 | 5.0 |
Twenty four | 12 | 1920 × 1080 | 24.7 | 16.625 | 30 | 5 | 3.0 |
Twenty five | 13 | 1920 × 1080 | 32.4 | 20.14 | 30 | 1 | 2.0 |
Twenty six | 08 | 1920 × 1080 | 19.6 | 19.65 | 29 | 1 | 3.0 |
Twenty seven | 07 | 1920 × 1080 | 18.7 | 18.67 | 29 | 2 | 2.0 |
Twenty eight | 08 | 1920 × 1080 | 19.7 | 19.75 | 30 | 6 | 5.0 |
Twenty nine | 08 | 1920 × 1080 | 19.9 | 19.92 | 30 | 1 | 1.0 |
Thirty | 07 | 1920 × 1080 | 17.2 | 19.67 | 30 | 2 | 2.0 |
Rights and permissions
About this article
Cite this article
Tyagi, R., Tomar, G.S. & Shrivastava, L. Unconstrained Face Detection of Multiple Humans Present in the Video. Wireless Pers Commun 118, 901–917 (2021). https://doi.org/10.1007/s11277-020-08050-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-020-08050-2