Abstract
In this paper examines effectiveness of HAAR feature-based cascade classifier for face detection in the presence of various image distortions. In the article we have focused on picture distortions that are likely to be met in everyday life, namely blurring, salt and pepper noise, contrast and brightness shifts and “fisheye” type distortion typical for wide-angle lens. In the paper present the mathematical model of the classifier and distortions, the training procedure and finally results of segmentation under various level of distortion. The test dataset is a large publicly available “Labelled Faces in the Wild” (LFW). Results show that Cascade Classifier finds it most difficult to recognize images that contain 70% noise type salt and pepper. The least impact on the effectiveness of the method use of blurred images even though the high parameter of blurring. From the obtained results it appears that the effectiveness of face detection is also affected by the adequate parameters of contrast and brightness.
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References
Voila, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (ICCVPR 2001), 8–14 December 2001, Kauai, USA, vol. 1, pp. I-511–I-518 (2001)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Ma, S., Bai, L.: A face detection algorithm based on Adaboost and new Haar-Like feature. In: IEEE International Conference on Software Engineering and Service Science, 23 March 2017, pp. 651–654 (2017)
Li, C., Qi, Z., Jia, N., Wu, J.: Human face detection algorithm via Haar cascade classifier combined with three additional classifiers. In: IEEE International Conference on Electronic Measurement & Instruments (ICEMI), pp. 483–487 (2017)
Joshi, P., Prakash, S.: Image quality assessment based on noise detection. In: International Conference on Signal Processing and Integrated Networks (SPIN), 24 March 2014, pp. 755–759 (2014)
Fitriyani, N.L., Yang, C.-K., Syafrudin, M.: Real-time eye state detection system using Haar cascade classifier and Circular Hough Transform. In: IEEE 5th Global Conference on Consumer Electronics, 29 December 2016, pp. 1–3 (2016)
Xiang, B., Cheng, X.: Eye detection based on improved AD AdaBoost algorithm. In: International Conference on Signal Processing Systems, 23 August 2010, pp. V2-617–V2-620 (2010)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst (2007)
Zhou, Y., Liu, D., Huang, T.: Survey of face detection on low-quality images. In: IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 07 June 2018, pp. 769–773 (2018)
Grm, K., Štruc, V., Artiges, A., Caron, M., Ekenel, H.K.: Strengths and weaknesses of deep learning models for face recognition against image degradations. IET Biometrics 7, 81–89 (2018)
Budiman, R.A.M., Achmad, B., Faridah, Arif, A., Nopriadi, Zharif, L.: Localization of white blood cell images using Haar cascade classifiers. In: 1st International Conference on Biomedical Engineering (IBIOMED), 20 March 2017
Sawas, J., Petillot, Y., Pailhas, Y.: Cascade of boosted classifiers for rapid detection of underwater objects. In: ECUA 2010, Istanbul (2010)
Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: IEEE CVPR (2001)
Gu, K., Zhai, G., Liu, M., Min, X., Yang, X., Zhang, W.: Brightness preserving video contrast enhancement using S-shaped transfer function. In: 2013 Visual Communications and Image Processing (VCIP), 09 January 2014 (2014)
Dong, X., Zhang, Y., Liu, J., Hu, G.: A fisheye image barrel distortion correction method of the straight slope constraint. In: 2015 8th International Congress on Image and Signal Processing (CISP), 18 February 2016 (2016)
Miziolek, W., Sawicki, D.: Face recognition: PCA or ICA. Przeglad Elektrotechniczny 88(7a), 286–288 (2012)
Siwek, K., Osowski, S.: Comparison of methods of feature generation for face recognition. Przeglad Elektrotechniczny 90(4), 206–209 (2014)
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Mazurek, P., Hachaj, T. (2020). Robustness of Haar Feature-Based Cascade Classifier for Face Detection Under Presence of Image Distortions. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_3
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DOI: https://doi.org/10.1007/978-3-030-31254-1_3
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