Face Detection Analysis of Digital Photos Using Mean Filtering Method

(1) Sunardi Sunardi Mail (Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta,, Indonesia)
(2) Anton Yudhana Mail (Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta,, Indonesia)
(3) * Setiawan Ardi Wijaya Mail (Universitas Ahmad Dahlan, Indonesia)
*corresponding author


Face detection in digital photos aims to get the face area in the digital photo. Usually, a lot of noise occurred when detecting faces in digital photos. This study applies the mean filtering method to improve digital photos by reducing noise. The accuracy of the mean filtering method is calculated using a confusion matrix, while the ability of this method is measured using the parameters of Mean Square Error (MSE) and Peak Noise to Signal Ratio (PNSR). Viola-Jones method was used to detect faces in this research. This method was chosen because it is one of the face detection procedures with high accuracy and good computational ability. Testing the mean filtering method obtained the lowest MSE of 9.33, while the highest PNSR of 14.37. The accuracy obtained by the mean filtering method using confusion is 90%. Based on these results, it can be concluded that the mean filtering method is feasible to be used in the case of face detection in digital photos.


Mean Filtering; viola-jones; MSE; PNSR; Confusion Matrix




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A. Eleyan and M. S. Anwar, “Multiresolution Edge Detection Using Particle Swarm Optimization,” Int. J. Eng. Sci. Appl., vol. 1, no. 1, pp. 11–17, 2017.

M. R. Wankhade and N. M. Wagdarikar, “Feature Extraction of Edge Detected Images,” Int. J. Comput. Sci. Mob. Comput., vol. 6, no. 6, pp. 336–345, 2017.

M. V. Alyushin, V. M. Alyushin, and L. V. Kolobashkina, “Optimization of the data representation integrated form in the viola-jones algorithm for a person’s face search,” Procedia Comput. Sci., vol. 123, pp. 18–23, 2018, doi: 10.1016/j.procs.2018.01.004.

N. T. Deshpande and S. Ravishankar, “Face Detection and Recognition using Viola-Jones algorithm and fusion of LDA and ANN,” IOSR J. Comput. Eng., vol. 18, no. 6, pp. 1–6, 2016, [Online]. Available: https://pdfs.semanticscholar.org/c5cf/c1f5a430ad9c103b381d016adb4cba20ce4e.pdf

A. Wedianto, H. L. Sari, and Y. S. H, “Analisa Perbandingan Metode Filter Gaussian, Mean Dan Median Terhadap Reduksi Noise,” J. Media Infotama, vol. 12, no. 1, pp. 21–30, 2016, doi: 10.37676/jmi.v12i1.269.

R. Kumar, C. Shao, and P. Kaur, “An improved adaptive weighted mean filtering approach for metallographic image processing,” J. Intell. Syst., vol. 30, no. 1, pp. 470–478, 2021, doi: 10.1515/jisys-2020-0080.

J. Choudhary and A. Choudhary, “Enhancement in Morphological Mean Filter for Image Denoising Using GLCM Algorithm,” Int. J. Comput. Theory Eng., vol. 13, no. 4, pp. 134–137, 2021, doi: 10.7763/ijcte.2021.v13.1302.

S. A. Shaban and D. L. Elsheweikh, “Blood group classification system based on image processing techniques,” Intell. Autom. Soft Comput., vol. 31, no. 2, pp. 817–834, 2022, doi: 10.32604/iasc.2022.019500.

S. Maharani, H. Ridwanto, H. Rahmania Hatta, D. Marisa Khairina, and M. Rivani Ibrahim, “Comparison of TOPSIS and MAUT methods for recipient determination home surgery,” IAES Int. J. Artif. Intell., vol. 10, no. 4, p. 930, 2021, doi: 10.11591/ijai.v10.i4.pp930-937.

K. Jankowska and P. Ewert, “Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive,” Power Electron. Drives, vol. 6, no. 1, pp. 100–112, 2021, doi: 10.2478/pead-2021-0008.

V. Balaji and S. K. S. Raja, “Recommendation learning system model for children with autism,” Intell. Autom. Soft Comput., vol. 31, no. 2, pp. 1301–1315, 2022, doi: 10.32604/iasc.2022.020287.

D. G. Chachlakis, T. Zhou, F. Ahmad, and P. P. Markopoulos, “Minimum Mean-Squared-Error autocorrelation processing in coprime arrays,” Digit. Signal Process. A Rev. J., vol. 114, p. 103034, 2021, doi: 10.1016/j.dsp.2021.103034.

P. Pinki and R. Mehra, “Estimation of the Image Quality under Different Distortions,” Int. J. Eng. Comput. Sci., vol. 5, no. 17291, pp. 17291–17296, 2016, doi: 10.18535/ijecs/v5i7.20.

D. Umamaheswari and E. Karthikeyan, “Comparative analysis of various filtering techniques in image processing,” Int. J. Sci. Technol. Res., vol. 8, no. 9, pp. 109–114, 2019.

M. A. Abdillah, A. Yudhana, and A. Fadlil, “Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods,” JUITA J. Inform., vol. 9, no. 1, p. 43, 2021, doi: 10.30595/juita.v9i1.8559.

I. Riadi, A. Yudhana, and W. Y. Sulistyo, “Analisis Perbandingan Nilai Kualitas Citra pada Metode Deteksi Tepi,” Rekayasa Sist. dan Teknol. Inf., vol. 4, no. 2, pp. 345–351, 2020.

E. A. Pambudi, E. S. Wijaya, and A. Fauzan, “Improved Sauvola Threshold for Background Subtraction on Moving Object Detection,” Int. J. Softw. Eng. Comput. Syst., vol. 5, no. 2, pp. 78–89, 2019, doi: 10.15282/ijsecs.5.2.2019.6.0062.

N. Senthilkumaran and S. Vaithegi, “Image Segmentation By Using Thresholding Techniques For Medical Images,” Comput. Sci. Eng. An Int. J., vol. 6, no. 1, pp. 1–13, 2016, doi: 10.5121/cseij.2016.6101.

A. Kaur, U. Rani, and G. S. Josan, “Modified Sauvola binarization for degraded document images,” Eng. Appl. Artif. Intell., vol. 92, no. March, p. 103672, 2020, doi: 10.1016/j.engappai.2020.103672.

A. Yudhana, Sunardi, and S. Saifullah, “Segmentation comparing eggs watermarking image and original image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 47–53, 2017, doi: 10.11591/eei.v6i1.595.

P. Irgens, C. Bader, T. Lé, D. Saxena, and C. Ababei, “An efficient and cost effective FPGA based implementation of the Viola-Jones face detection algorithm,” HardwareX, vol. 1, pp. 68–75, 2017, doi: 10.1016/j.ohx.2017.03.002.

Syafira, A. Rizkita, and G. Ariyanto, “Sistem Deteksi Wajah Dengan Modifikasi Metode Viola Jones,” Emit. J. Tek. Elektro, vol. 17, no. 1, pp. 26–33, 2017, doi: 10.23917/emitor.v17i1.5964.

I. Riadi, A. Fadlil, and T. Sari, “Image Forensic for detecting Splicing Image with Distance Function,” Int. J. Comput. Appl., vol. 169, no. 5, pp. 6–10, 2017, doi: 10.5120/ijca2017914729.

M. Zairi, T. Boujiha, and A. Ouelli, “Improved JPEG image watermarking in data compression domain using block selection strategy,” EAI Endorsed Trans. Internet Things, vol. 6, no. 24, p. 168690, 2021, doi: 10.4108/eai.8-2-2021.168690.

S. Battiato, O. Giudice, F. Guarnera, and G. Puglisi, “Estimating Previous Quantization Factors on Multiple JPEG Compressed Images,” Eurasip J. Inf. Secur., vol. 2021, no. 1, 2021, doi: 10.1186/s13635-021-00120-7.

S. Saifullah, S. Sunardi, and A. Yudhana, “Analisis Perbandingan Pengolahan Citra Asli Dan Hasil Croping Untuk Identifikasi Telur,” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 3, pp. 341–350, 2016, doi: 10.28932/jutisi.v2i3.512.

S. Attaway, Introduction to MATLAB. 2019. doi: 10.1016/b978-0-12-815479-3.00001-5.

A. Vagga, S. Aherrao, H. Pol, and V. Borkar, “Flow visualization by Matlab® based image analysis of high-speed polymer melt extrusion film casting process for determining necking defect and quantifying surface velocity profiles,” Adv. Ind. Eng. Polym. Res., no. xxxx, 2021, doi: 10.1016/j.aiepr.2021.02.003.

M. Coblenz, “MATVines: A vine copula package for MATLAB,” SoftwareX, vol. 14, p. 100700, 2021, doi: 10.1016/j.softx.2021.100700.

P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” no. February, 2001, doi: 10.1109/CVPR.2001.990517.

P. Viola and M. Jones, “Robust Real-Time Face Detection Robust Real-time Face Detection,” no. June, pp. 2–3, 2014, doi: 10.1023/B.

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