Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

  • Verry Noval Kristanto Universitas Islam Indonesia
  • Imam riadi Universitas Ahmad Dahlan
  • Yudi Prayudi Universitas Islam Indonesia
Keywords: face forensic, low-quality image, face detection, face recognition


Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD),  Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.


Download data is not yet available.

Author Biographies

Imam riadi, Universitas Ahmad Dahlan

Department of Information System

Yudi Prayudi, Universitas Islam Indonesia

Department of Informatics


A. Nagila, R. Nagila, and S. Bhardwaj, “Advanced Face Recognition for Controlling Crime Using PCA,” International Journal Advanced Research Engineering a Technology (IJARET) of in nd, vol. 12, no. 2, pp. 657–663, 2021, doi: 10.34218/IJARET.12.2.2021.064.

Y. Gao, L. Gao, and X. Li, “A Generative Adversarial Network Based Deep Learning Method for Low-Quality Defect Image Reconstruction and Recognition,” IEEE Trans Industr Inform, vol. 17, no. 5, pp. 3231–3240, May 2021, doi: 10.1109/TII.2020.3008703.

Y. Liu, G. Zhai, X. Liu, and D. Zhao, “Quality Assessment for Out-of-Focus Blurred Images,” in 2015 Visual Communications and Image Processing (VCIP), 2015. Accessed: Sep. 27, 2022. [Online]. Available:

A. Chulichkov and E. Molkov, “Increasing the resolution of a non-negative brightness image distorted by a linear transformation,” in Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology, May 2020. doi: 10.1109/ITNT49337.2020.9253301.

Q. Lu and P. Gan, “Low-Light Face Recognition and Identity Verification Based on Image Enhancement,” Traitement du Signal, vol. 39, no. 2, pp. 513–519, Apr. 2022, doi: 10.18280/ts.390213.

M. Rakhshanfar and M. A. Amer, “Low-Frequency Image Noise Removal Using White Noise Filter.,” 25th IEEE International Conference on Image Processing (ICIP), pp. 3948–3952, 2018.

C. Tang et al., “Joint Regularized-based Image Reconstruction by Combining Super-Resolution Sinogram for Computed Tomography Imaging,” in Proceedings - 2020 5th International Conference on Communication, Image and Signal Processing, CCISP 2020, Nov. 2020, pp. 188–193. doi: 10.1109/CCISP51026.2020.9273488.

W. Xiong, “Research on Fire Detection and Image Information Processing System Based on Image Processing,” in Proceedings - 2020 International Conference on Advance in Ambient Computing and Intelligence, ICAACI 2020, Sep. 2020, pp. 106–109. doi: 10.1109/ICAACI50733.2020.00027.

C. Li, J. Guo, S. Chen, Y. Tang, Y. Pang, and J. Wang, “Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging,” 2016 IEEE International Conference on Image Processing (ICIP), pp. 1993–1997, 2016.

Suhas. S and Venugopal C R, “MRI Image preprocessing and Noise removal technique using linear and nonlinear filters,” in 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Feb. 2017, pp. 1–4.

V. Voronin, E. Semenishchev, O. Balabaeva, and M. Pismenskova, “Preprocessing Images And Restore The Contours Of Objects Obtained In The Infrared Range,” in 2018 14th IEEE International Conference on Signal Processing (ICSP), Feb. 2018, pp. 430–433.

P. Pandey, K. K. Dewangan, and D. K. Dewangan, “Enhancing the Quality of Satellite Images by Preprocessing and Contrast Enhancement,” in 2017 International Conference on Communication and Signal Processing (ICCSP), Apr. 2017, pp. 56–60.

B. Furht, E. Akar, and A. Andrews, Digital Image Processing: Practical Approach. Florida: Springer, 2018. [Online]. Available:

W. Y. Lu and M. Yang, “Face detection based on viola-jones algorithm applying composite features,” in Proceedings - 2019 International Conference on Robots and Intelligent System, ICRIS 2019, Jun. 2019, pp. 82–85. doi: 10.1109/ICRIS.2019.00029.

Z. M. Abood, G. S. Karam, and R. E. Haleot, “Face Recognition Using Fusion of Multispectral Imaging,” 2017 2nd Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications, AIC-MITCSA 2017, pp. 107–112, 2019.

P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” Rapid Object Detection using a Boosted Cascade of Simple Features, pp. 511–518, 2001.

X. Yang, N. Liang, W. Zhou, and H. Lu, “A face detection method based on skin color model and improved adaboost algorithm,” Traitement du Signal, vol. 37, no. 6, pp. 929–937, Dec. 2020, doi: 10.18280/TS.370606.

S. N. Borade, R. R. Deshmukh, and P. Shrishrimal, “Effect of Distance Measures on the Performance of Face Recognition Using Principal Component Analysis,” Advances in Intelligent Systems and Computing, vol. 384, pp. 247–257, 2016, doi: 10.1007/978-3-319-23036-8_50.

Ahmed ElSayed, Ausif Mahmood, and Tarek Sobh, “Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild,” in 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Oct. 2018, pp. 1–5.

H. Proenca, J. C. Neves, S. Barra, T. Marques, and J. C. Moreno, “Joint Head Pose/Soft Label Estimation for Human Recognition In-The-Wild,” IEEE Trans Pattern Anal Mach Intell, vol. 38, no. 12, pp. 2444–2456, Dec. 2016, doi: 10.1109/TPAMI.2016.2522441.

M. Knoche, S. Hormann, and G. Rigoll, “Cross-Quality LFW: A Database for Analyzing Cross- Resolution Image Face Recognition in Unconstrained Environments,” in 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), Dec. 2021, pp. 1–5. doi: 10.1109/FG52635.2021.9666960.

N. H. Barnouti, M. H. N. Al-Mayyahi, and S. S. M. Al-Dabbagh, “Real-Time Face Tracking and Recognition System Using Kanade-Lucas-Tomasi and Two-Dimensional Principal Component Analysis,” in 2018 International Conference on Advanced Science and Engineering (ICOASE), Oct. 2018, pp. 24–29.

A. Matin, F. Mahmud, and M. T. B. Shawkat, “Recognition of an Individual using the Unique Features of Human face,” in 2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), Dec. 2016, pp. 57–60.

G. Blanchet and M. Charbit, Digital Signal and Image Processing using MATLAB, Volume 2: Advances and Applications: The Deterministic Case, 2nd ed., vol. 2. Wiley-ISTE, 2015.

D. Faroek, Rusydi Umar, and Imam Riadi, “Classification Based on Machine Learning Methods for Identification of Image Matching Achievements,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 198–206, Apr. 2022, doi: 10.29207/resti.v6i2.3826.

A. Lopez F.J, R. Avi J, and A. Fernandez M.V, “COMPLETE CONTROL OF AN OBSERVED CONFUSION MATRIX,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Nov. 2018, pp. 1222–1223.

S. S. Goilkar and D. M. Yadav, “Implementation of blind and non-blind deconvolution for restoration of defocused image,” in 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, Mar. 2021, pp. 560–563. doi: 10.1109/ESCI50559.2021.9397046.

G. George, R. M. Oommen, S. Shelly, S. S. Philipose, and A. M. Varghese, “A Survey on Various Median Filtering Techniques For Removal of Impulse Noise From Digital Image,” in 2018 Conference on Emerging Devices and Smart Systems (ICEDSS), Mar. 2018, pp. 235–238.

B. Oktavianto and T. W. Purboyo, “A Study of Histogram Equalization Techniques for Image Enhancement,” International Journal of Applied Engineering Research, vol. 13, no. 2, pp. 1165–1170, 2018, [Online]. Available:

B. Fatima, A. R. Shahid, S. Ziauddin, A. A. Safi, and H. Ramzan, “Driver Fatigue Detection Using Viola Jones and Principal Component Analysis,” Applied Artificial Intelligence, vol. 34, no. 6, pp. 456–483, 2020, doi: 10.1080/08839514.2020.1723875.

D. Shamia and D. A. Chandy, “Analyzing the performance of Viola Jones Face Detector on the LDHF database,” Proceedings of IEEE International Conference on Signal Processing and Communication, ICSPC 2017, vol. 2018-Janua, no. July, pp. 312–315, 2018, doi: 10.1109/CSPC.2017.8305860.

K. Dang and S. Sharma, “Review and Comparison of Face Detection Algorithms,” in 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, Jan. 2017, pp. 629–633.

N. A. Abdullah, M. J. Saidi, N. H. A. Rahman, C. C. Wen, and I. R. A. Hamid, “Face recognition for criminal identification: An implementation of principal component analysis for face recognition,” in AIP Conference Proceedings, Oct. 2017, vol. 1891. doi: 10.1063/1.5005335.

How to Cite
Kristanto, V. N., Riadi, I., & Prayudi, Y. (2023). Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 218 - 225.
Artikel Rekayasa Sistem Informasi