Document Type : Research Paper
Authors
1 Department of Master of Computer Application, Jain University, Knowledge Campus, Bengalore, Karnataka, India.
2 Department of Master of Computer Application Jain University, Knowledge Campus, Bengaluru, Karnataka, India.
Abstract
Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the face. The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’t be solved by any of the existing methods but can be solved by our proposed method.
Keywords
Main Subjects
[1] Mohapatra, H. I. T. E. S. H. (2009). HCR using neural network (PhD dissertation Biju Patnaik University of Technology).
[2] Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT. IET wireless sensor systems, 9(6), 447-457.
[3] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET wireless sensor systems, 9(6), 358-365.
[4] Mohapatra, H., Debnath, S., & Rath, A. K. (2019). Energy management in wireless sensor network through EB-LEACH. International journal of research and analytical reviews (IJRAR), 56-61.
[5] Nirgude, V., Mahapatra, H., & Shivarkar, S. (2017). Face recognition system using principal component analysis & linear discriminant analysis method simultaneously with 3d morphable model and neural network BPNN method. Global journal of advanced engineering technologies and sciences, 4(1), 1-6.
[6] Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in heterogeneous environment. International journal of scientific & technology research, 8, 1009-1013.
[7] Mohapatra, H., & Rath, A. K. (2019). Fault-tolerant mechanism for wireless sensor network. IET wireless sensor systems, 10(1), 23-30.
[8] Swain, D., Ramkrishna, G., Mahapatra, H., Patr, P., & Dhandrao, P. M. (2013). A novel sorting technique to sort elements in ascending order. International journal of engineering and advanced technology, 3(1), 212-126.
[9] Broumi, S., Dey, A., Talea, M., Bakali, A., Smarandache, F., Nagarajan, D., ... & Kumar, R. (2019). Shortest path problem using Bellman algorithm under neutrosophic environment. Complex & intelligent systems, 5(4), 409-416.
[10] Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems, 24, 134-149.
[11] Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global.
[12] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. International journal of engineering and advanced technology, 8(3), 347-353.
[13] Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering, 8(6), 724-731.
[14] Kumar, R., Edaltpanah, S. A., Jha, S., & Broumi, S. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems, 23(1), 2.
[15] Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA), 9(2), 132-161.
[16] Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering, 4(1), 1-7.
[17] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255-263.
[18] Pratihar, J., Kumar, R., Dey, A., & Broumi, S. (2020). Transportation problem in neutrosophic environment. In Neutrosophic graph theory and algorithms (pp. 180-212). IGI Global.
[19] Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research, 8(4), 975-982.
[20] Pratihar, J., Kumar, R., Edalatpanah, S. A., & Dey, A. (2020). Modified Vogel’s approximation method for transportation problem under uncertain environment. Complex & intelligent systems, 1-12.
[21] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple t-norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.
[22] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic subgroup. In Neutrosophic graph theory and algorithms (pp. 213-259). IGI Global.
[23] Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology, 8, 385-390.
[24] Roy, S., Roy, S., & Bandyopadhyay, S. K. (2012). A tutorial review on face detection. Intl. J. of engineering research & technology, 1(8), 10.
[25] Singh, V., Shokeen, V., & Singh, B. (2013). Face detection by Haar cascade classifier with simple and complex backgrounds images using opencv implementation. International journal of advanced technology in engineering and science, 1(12), 33-38.
[26] Roy, S., & Podder, S. (2013). Face detection and its applications. International journal of research in engineering & advanced technology, 1(2), 1-10.
[27] Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. Retrieved from https://ora.ox.ac.uk/objects/uuid:a5f2e93f-2768-45bb-8508-74747f85cad1/download_file?file_format=pdf&safe_filename=parkhi15.pdf&type_of_work=Conference+item
[28] Tolba, A. S., El-Baz, A. H., & El-Harby, A. A. (2006). Face recognition: a literature review. International journal of signal processing, 2(2), 88-103.
[29] Sun, X., Wu, P., & Hoi, S. C. (2018). Face detection using deep learning: an improved faster RCNN approach. Neurocomputing, 299, 42-50. https://doi.org/10.1016/j.neucom.2018.03.030
[30] Puri, R., Gupta, A., Sikri, M., Tiwari, M., Pathak, N., & Goel, S. (2018). Emotion detection using image processing in python. In 5th international conference on “computing for sustainable global development (IndiaCom)” IEEE conference ID (Vol. 42835).
[31] Klug, A., & De Rosier, D. J. (1966). Optical filtering of electron micrographs: reconstruction of one-sided images. Nature, 212(5057), 29-32.
[32] Billingsley, F. C. (1970). Applications of digital image processing. Applied optics, 9(2), 289-299.
[33] Andrews, H., & Patterson, C. (1976). Singular value decompositions and digital image processing. IEEE transactions on acoustics, speech, and signal processing, 24(1), 26-53.
[34] Goetcherian, V. (1980). From binary to grey tone image processing using fuzzy logic concepts. Pattern recognition, 12(1), 7-15.
[35] Burt, P. J. (1981). Fast filter transform for image processing. Computer graphics and image processing, 16(1), 20-51.
[36] Sternberg, S. R. (1983). Biomedical image processing. Computer, (1), 22-34.
[37] Umbaugh, S. E. (1997). Computer vision and image processing: a practical approach using cviptools with cdrom. Prentice Hall PTR.
[38] Lehmann, T. M., Gonner, C., & Spitzer, K. (1999). Survey: Interpolation methods in medical image processing. IEEE transactions on medical imaging, 18(11), 1049-1075.
[39] Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., ... & Marconcini, M. (2009). Recent advances in techniques for hyperspectral image processing. Remote sensing of environment, 113, S110-S122.
[40] Viola, P., & Jones, M. (2001, December). 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. CVPR 2001 (Vol. 1, pp. I-I). IEEE.
[41] Lienhart, R., & Maydt, J. (2002, September). An extended set of Haar-like features for rapid object detection. In Proceedings international conference on image processing (Vol. 1, pp. I-I). IEEE.
[42] Messom, C., & Barczak, A. (2006, December). Fast and efficient rotated Haar-like features using rotated integral images. In Australian conference on robotics and automation (pp. 1-6).
[43] Haselhoff, A., & Kummert, A. (2009, June). A vehicle detection system based on Haar and triangle features. In 2009 IEEE intelligent vehicles symposium (pp. 261-266). IEEE.
[44] Angriani, L., Dayat, A. R., & Amin, S. (2014). Implementation method viola jones for detection many faces. International conference on computer systems (ICCS). Makassar, South Sulawesi, Indonesia.
[45] AbdelRaouf, A., Higgins, C. A., Pridmore, T., & Khalil, M. I. (2016). Arabic character recognition using a Haar cascade classifier approach (HCC). Pattern analysis and applications, 19(2), 411-426.
[46] Daliman, S., Abu-Bakar, S. A. R., & Azam, M. N. (2016, June). Development of young oil palm tree recognition using Haar-based rectangular windows. IOP conference series: earth and environmental science (Vol. 37, No. 1, p. 012041).
[47] Meduri, P., & Telles, E. (2018). A Haar-Cascade classifier based Smart Parking System. Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) (pp. 66-70). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
[48] Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE transactions on pattern analysis and machine intelligence, 20(1), 23-38.
[49] Chitra, S., & Balakrishnan, G. (2012). Comparative study for two color spaces HSCbCr and YCbCr in skin color detection. Applied mathematical sciences, 6(85), 4229-4238.
[50] Bhat, V. S., & Pujari, D. J. (2013, September). Face detection system using HSV color model and morphing operations. Proceedings of national conference on women in science & engineering (NCWSE’13) (pp. 200-204). SDMCET Dharwad.
[51] Singhraghuvanshi, D., & Agrawal, D. (2012, January). Human face detection by using skin color segmentation, face features and regions properties. International journal of computer applications (IJCA), 38(9), pp. 14-17.
[52] Tripathi, S., Sharma, V., & Sharma, S. (2011). Face detection using combined skin color detector and template matching method. International journal of computer applications, 26(7), 5-8.
[53] Amit, Y., Geman, D., & Wilder, K. (1997). Joint induction of shape features and tree classifiers. IEEE transactions on pattern analysis and machine intelligence, 19(11), 1300-1305.
[54] Face-agency. (2020). Retrieved from https://www.face-agency.co.uk/
[55] 123RF. (2020). Retrieved from https://www.123rf.com
[56] Dwivedi, D. (2018). Face detection for beginners. Retrieved from https://towardsdatascience.com
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