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There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into the main sixteen blocks. Each block of these sixteen blocks is divided into more to thirty sub-blocks. For each sub-block, the SVD transformation is applied, and the norm of the diagonal matrix is calculated, which is used to create the 16x30 feature matrix. The sub-blocks of two images, (thirty elements in the main block) are compared with others using the Euclidean distance. The minimum value for each main block is selected to be one feature input to the neural network. Classification is implemented by a backpropagation neural network, where a 16-feature matrix is used as input to the neural network. The performance of the current proposal was up to 97% when using the FEI (Brazilian) database. Moreover, the performance of this study is promised when compared with recent state-of-the-art approaches and it solves some of the challenges such as illumination and facial expression.
Published Online First 20/3/2022
This work is licensed under a Creative Commons Attribution 4.0 International License.
Razzaq AN, Hussain Z, Mohammed HR. Structural Geodesic-Tchebychev Transform: An image similarity measure for face recognition. JCS; 2016; not complete?
Zhang X, Zhao H. Hyperspectral-cube-based mobile face recognition: A comprehensive review. Information Fusion (Inf Fusion). Elsevier; 2021;
Wang M, Deng W. Deep face recognition: A survey. Neurocomputing. 2021;429:215–44.
Madhavan S, Kumar N. Incremental methods in face recognition: a survey. Artif Intell Rev. Springer; 2021;54(1):253–303.
Horkaew P, Khaminkure A, Suesat N, Puttinaovarat S. Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model. Baghdad Sci. J.; 2020;17(1).
Hassan NF, Abdulrazzaq HI. Pose invariant palm vein identification system using convolutional neural network. Baghdad Sci. J.; 2018;15(4).
Dargan S, Kumar M. A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. EXPERT SYST APPL. 2020;143:113114.
Singh M, Singh R, Ross A. A comprehensive overview of biometric fusion. Inf Fusion. Elsevier; 2019;52:187–205.
Ramakrishnan S. Pattern Recognition: Analysis and Applications. IntechOpen; 2016.
Al Naffakh HAH, Ghazali R, El Abbadi NK, Razzaq AN. A review of human skin detection applications based on image processing. BEEI. 2021;10(1):129–37.
Wang J-W, Le NT, Lee J-S, Wang C-C. Color face image enhancement using adaptive singular value decomposition in fourier domain for face recognition. Pattern Recognition. Elsevier; 2016;57:31–49.
Hu C, Lu X, Ye M, Zeng W. Singular value decomposition and local near neighbors for face recognition under varying illumination. Pattern Recognition. Elsevier; 2017;64:60–83.
Loderer M, Pavlovicova J, Oravec M. Comparative Study of Local Binary Pattern Derivatives for Low Size Feature Vector Representation in Face Recognition. APH. 2018;15(4).
Harakannanavar SS, Prashanth CR, Patil S, Raja KB. Face recognition based on swt, dct and ltp. In: Integrated Intelligent Computing, Communication and Security. Springer; 2019. p. 565–73.
Kakarwal SN, Chaudhari KP, Deshmukh RR, Patil RB. Thermal Face Recognition using Artificial Neural Network. In: 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC). IEEE; 2020. p. 300–4.
Baker SA, Mohammed HH, Aldabagh HA. Improving face recognition by artificial neural network using principal component analysis. TELKOMNIKA. Ahmad Dahlan University; 2020;18(6):3357–64.
Yang W, Wang Z, Zhang B. Face recognition using adaptive local ternary patterns method. Neurocomputing. Elsevier; 2016;213:183–90.
Zhang Y, Hu C, Lu X. Face recognition under varying illumination based on singular value decomposition and retina modeling. MTA; 2018;77(21):28355–74.
El Abbadi NK, Saleem E. Image Colorization Based on GSVD and YCbCr Color Space. KJS. 2019;46(4).
Aria EH, Amini J, Saradjian MR. Back propagation neural network for classification of IRS-1D satellite images. In: Joint Workshop of High Resolution Mapping from Space, Tehran University, Iran. 2003.
Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE NEUR NET LEAR; 1994;5(6):989–93.
This database is publicly available on. :http://www.fei.edu.br/~cet/facedatabase.html.
Suma SL, Raga S. Real time face recognition of human faces by using LBPH and Viola Jones algorithm. IJSRCSE. 2018;6(5):6–10.
Salah SK, Humood WR, Khalaf AO. A Proposed Generalized Eigenfaces System for Face Recognition Based on One Training Image. JSJU. 2020;55(2).
Ghazal MT, Abdullah K. Face recognition based on curvelets, invariant moments features and SVM. TELKOMNIKA. Ahmad Dahlan University; 2020;18(2):733–9.
Mohammed IM, Al-Dabagh MZN, Ahmad MI, Isa MNM. Face Recognition Using PCA Implemented on Raspberry Pi. In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 (NUSYS’19). Springer; 2021. p. 873–89.
Lv X, Su M, Wang Z. Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems. Microprocessors and Microsystems: Embedded Hardware Design. Elsevier; 2021;104034.
Chandrakala M, Devi PD. Two-stage classifier for face recognition using HOG features. MATER TODAY-PROC. Elsevier; 2021.