Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval
(1) Institut Informatika dan Bisnis Darmajaya
(2) Institut Informatika dan Bisnis Darmajaya
(3) Institut Informatika dan Bisnis Darmajaya
(4) Institut Informatika dan Bisnis Darmajaya
Abstract
Purpose: Face recognition is a geometric space recording activity that allows it to be used to distinguish the features of a face. Therefore, facial recognition can be used to identify ID cards, ATM card PINs, search for one’s committed crimes, terrorists, and other criminals whose faces were not caught by Close-Circuit Television (CCTV). Based on the face image database and by applying the Content-Base Image Retrieval method (CBIR), committed crimes can be recognized on his face. Moreover, the image segmentation technique was carried out before CBIR was applied. This work tried to recognize an individual who committed crimes based on his or her face by using sketch facial images as a query. Methods: We used an image sketch as a querybecause CCTV could not have caught the face image. The research used no less than 1,000 facial images were carried out, both normal as well asabnormal faces (with obstacles). Findings:Experiments demonstrated good enough in terms of precision and recall, which are 0,8 and 0,3 respectively, which is better than at least two previous works.The work demonstrates a precision of 80% which means retrieval of effectiveness is good enough. The 75 queries were carried out in this work to compute the precision and recall of image retrieval. Novelty: Most face recognition researchers using CBIR employed an image as a query. Furthermore, previous work still rarely applied image segmentation as well as CBIR.
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K. Lin et al., “Face Detection and Segmentation Based on Improved Mask R-CNN,” Discret. Dyn. Nat. Soc., vol. 2020, 2020.
A. K. Jain, P. Flynn, and A. A. Ross, “Handbook of Biometrics Handbook of Biometrics,” 2007.
E. P. Purwandari, A. Erlansari, A. Wijanarko, and E. A. Adrian, “Face sketch recognition using principal component analysis for forensics application,” J. Teknol. dan Sist. Komput., vol. 8, no. 3, pp. 178–184, Jul. 2020.
M. Hu and J. Guo, “Facial attribute-controlled sketch-to-image translation with generative adversarial networks,” Eurasip J. Image Video Process., vol. 2020, no. 1, Dec. 2020.
W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, “Learning Low-Level Vision,” Int. J. Comput. Vis., vol. 40, no. 1, pp. 25–47, 2000.
G. Shakhnarovich and B. Moghaddam, “Face Recognition in Subspaces,” Springer-Verlag, 2004.
H. Kurniawan and T. Hidayat, “Perancangan Program Pengenalan Wajah Menggunakan Fungsi Jarak Metode Euclidean Pada Matlab,” Semin. Nas. Apl. Teknol. Inf. 2008 (SNATI 2008) Yogyakarta, 21 Juni 2008, vol. Vol 1, no. Snati, pp. 15–18, 2008.
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila, “Improved precision and recall metric for assessing generative models,” arXiv, no. NeurIPS, 2019.
Q. Liu, X. Tang, H. Jin, H. Lu, and S. Ma, “A Nonlinear Approach for Face Sketch Synthesis and Recognition,” IEEE Comput. Soc. Conf. Comput. Vis. pattern Recognit., vol. 1, pp. 1005–1010, 2005.
J. Alamri, R. Harrabi, and S. Ben Chaabane, “Face Recognition based on Convolution Neural Network and Scale Invariant Feature Transform,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 2, pp. 644–654, 2021.
S. Dalal, V. P. Vishwakarma, and S. Kumar, “Feature-based Sketch-Photo Matching for Face Recognition,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 562–570, 2020.
C. Galea and R. A. Farrugia, “Forensic Face Photo-Sketch Recognition Using a Deep Learning-Based Architecture,” IEEE Signal Process. Lett., vol. 24, no. 11, pp. 1586–1590, Nov. 2017.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” Proc. IEEE Conf. Comput. Vis. pattern Recognit., pp. 815–823, Mar. 2015.
L. Best-Rowden and A. K. Jain, “Learning Face Image Quality from Human Assessments,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 12, pp. 3064–3077, 2018.
M. S. Hasibuan and H. W. Nugroho, “Pemanfaatan Teknik Content Based Image Retrieval Berbasis Sketsa Untuk Pengenalan Wajah Dengan Pose Normal,” Konf. Nas. Sist. Inform. 2015, pp. 335–338, 2015.
K. Pandey, R. Lilani, P. Naik, and G. Pol, “Human Face Recognition Using Image Processing,” ICONECT’ 14 Conf. Proc., vol. 2, no. 4, pp. 283–288, 2014.
R. Dastres, M. Soori, A. Image, P. Systems, and I. Journal, “Advanced Image Processing Systems,” Int. J. Imaging Robot., vol. 21, no. 1, pp. 27–44, 2021.
J. Sun, Y. Wang, X. Wu, X. Zhang, and H. Gao, “A new image segmentation algorithm and its application in lettuce object segmentation,” Telkomnika, vol. 10, no. 3, pp. 557–563, 2012.
Z. Yuan, “Face Detection and Recognition Based on Visual Attention Mechanism Guidance Model in Unrestricted Posture,” Sci. Program., vol. 2020, 2020.
M. Alghaili, Z. Li, and H. A. R. Ali, “FaceFilter: Face Identification with Deep Learning and Filter Algorithm,” Sci. Program., vol. 2020, 2020.
J. Sujanaa and S. Palanivel, “Fusion of Deep-CNN and Texture Features for Emotion Recognition using Support Vector Machines,” Int. J. Eng. Res. Technol., vol. 9, no. 5, 2021.
Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey,” Sensors (Switzerland), vol. 20, no. 2. MDPI AG, Jan-2020.
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