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Suspect face retrieval using visual and linguistic information

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Abstract

Faces are the most common biometric used for the identification of a person. Law enforcement agencies use face as a key point to identify the suspect involved in unlawful activities. Forensic sketches are normally developed by the sketch artist based on verbal details provided by an eyewitness about the suspect. In a forensic sketch, the facial description depends on the memory of the eyewitness; therefore, there is uncertainty in facial attributes. In the recent past, lots of sketch-to-photograph retrieval methods are proposed by many researchers; however, they have ignored the uncertainty of facial attributes for suspect face retrieval. Recently, linguistic information is also utilized for suspect face retrieval. In this paper, we have provided an extensive review of the available methods for suspect face retrieval using visual and linguistic information. The review focuses firstly on the traditional methods and their categorization also shows the evolution of suspect face retrieval approaches over the years. We have also shown the summary of the performance of representative state-of-the-art methods.

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References

  1. Uhl, R. G., da Vitoria Lobo, N.: A framework for recognizing a facial image from a police sketch. In: Proceedings CVPR'96, 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996, p. 586593, IEEE, (1996)

  2. Identi-Kit, Identi-Kit Solutions 2011 [Online]. Available: http://www.identikit.net/

  3. Zahradnikova, B., Duchovicova, S., Schreiber, P.: Facial composite systems. Artif. Intell. Rev. 49(1), 131–152 (2018)

    Google Scholar 

  4. Jain, A.K., Li, S.Z.: Handbook of Face Recognition. Springer, Berlin (2011)

    MATH  Google Scholar 

  5. FACES 4.0, IQ Biometrix 2011 [Online]. Available: http://www.iqbiometrix.com

  6. Gibson, L.: Forensic Art Essentials: A Manual for Law Enforcement Artists. Academic Press, Cambridge (2010)

    Google Scholar 

  7. Taylor, K.T.: Forensic art and Illustration. CRC Press, Boca Raton (2000)

    Google Scholar 

  8. Lee, E., Whalen, T., Sakalauskas, J., Baigent, G., Bisesar, C., McCarthy, A., Reid, G., Wotton, C.: Suspect identification by facial features. Ergonomics 47(7), 719–747 (2004)

    Google Scholar 

  9. Sahin, Y.G., Basarici, S.M., Ercan, T.: Face matrix: a quick search and indexing method for suspect recognition in police departments. Inf. Technol. J. 6(4), 607–612 (2007)

    Google Scholar 

  10. Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1955–1967 (2008)

    Google Scholar 

  11. Zhang, W., Wang, X., Tang, X.: Coupled information-theoretic encoding for face photo-sketch recognition. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 513520, IEEE, (2011)

  12. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399458 (2003)

    Google Scholar 

  13. Benson, P.J., Perrett, D.I.: Perception and recognition of photographic quality facial caricatures: Implications for the recognition of natural images. Eur. J. Cognit. Psychol. 3(1), 105135 (1991)

    Google Scholar 

  14. Bruce, V., Hanna, E., Dench, N., Healey, P., Burton, M.: The importance of `mass’ in line drawings of faces. Appl. Cognit. Psychol. 6(7), 619628 (1992)

    Google Scholar 

  15. Bruce, V., Humphreys, G.W.: Recognizing objects and faces. Vis. Cognit. 1(2–3), 141180 (1994)

    Google Scholar 

  16. Davies, G., Ellis, H.D., Shepherd, J.: Face recognition accuracy as a function of mode of representation. J. Appl. Psychol. 63(2), 180 (1978)

    Google Scholar 

  17. Rhodes, G., Tremewan, T.: Understanding face recognition: caricauture effects, inversion, and the homogeneity problem. Vis. Cogni. 1(23), 275311 (1994)

    Google Scholar 

  18. Tang, X., Wang, X.: Face sketch recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 50–57 (2004)

    Google Scholar 

  19. Cheung, Y. M., Hu, Z.: A logical operator oriented face retrieval approach: how to identify a suspect using partial photo information from different persons? (2020)

  20. Xiaoou, T., Xiaogang, W.: Face photo recognition using sketch. In: Proceedings of the International Conference on Image Processing, vol. 1, pp. I-I. IEEE, (2002)

  21. Xiaoou, T., Xiaogang, W.: Face sketch synthesis and recognition. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 687–694. IEEE, (2003)

  22. Liu, Q., Tang, X., Jin, H., Lu, H., Ma, S.: A nonlinear approach for face sketch synthesis and recognition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, Vol. 1, pp. 1005–1010, IEEE, (2005)

  23. Li, Y.-H., Savvides, M., Bhagavatula, V.K.: Illumination tolerant face recognition using a novel face from sketch synthesis approach and advanced correlation filters. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, volume 2, pages II–II. IEEE, (2006)

  24. Zhang, W., Wang, X., Tang, X.: Lighting and pose robust face sketch synthesis. In: European Conference Computer Vision–ECCV 2010, pp. 420–433. Springer, (2010)

  25. Xinbo, G., Juanjuan, Z.J.L., Chunna, T.: Face sketch synthesis algorithm based on E-HMM and selective ensemble. Circuits and systems for video technology. IEEE Trans. 18(4), 487–496 (2008)

    Google Scholar 

  26. Xiao, B., Gao, X., Tao, D., Li, X.: A new approach for face recognition by sketches in photos. Signal Process. 89(8), 1576–1588 (2009)

    MATH  Google Scholar 

  27. Liu, W., Tang, X., Liu, J.: Bayesian tensor inference for sketch-based facial photo hallucination. In: IJCAI, pp. 2141–2146, (2007)

  28. Radman, A., Suandi, S.A.: A superpixel-wise approach for face sketch synthesis. IEEE Access 7, 108838–108849 (2019)

    Google Scholar 

  29. Wang, N., Zhu, M., Li, J., Song, B., Li, Z.: Data-driven vs. model-driven: fast face sketch synthesis. Neurocomputing 257, 214–221 (2017)

    Google Scholar 

  30. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp. 2672–2680, (2014)

  31. Isola, P., Zhu, J. Y., Zhou, T., Efros, A. A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134, (2017)

  32. Xian, W., Xu, K., Hall, P.: A survey of image synthesis and editing with generative adversarial networks. Tsinghua Sci. Technol. 22(6), 660–674 (2017)

    MATH  Google Scholar 

  33. Wang, N., Zha, W., Li, J., Gao, X.: ‘Back projection: An effective postprocessing method for GAN-based face sketch synthesis.’ Pattern Recognit. Lett. 107, 59–65 (2018)

    Google Scholar 

  34. Di, X., Patel, V. M.: Face synthesis from visual attributes via sketch using conditional VAEs and GANs. http://arxiv.org/abs/1801.00077, (2017)

  35. Zhang, S., Ji, R., Hu, J., Gao, Y., Chia-Wen, L.: Robust face sketch synthesis via generative adversarial fusion of priors and parametric sigmoid. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), pp. 1163–1169 (2018)

  36. Zhang, D., Lin, L., Chen, T., Wu, X., Tan, W., Izquierdo, E.: Content adaptive sketch portrait generation by decompositional representation learning. IEEE Trans. Image Process. 26(1), 328–339 (2016)

    MathSciNet  MATH  Google Scholar 

  37. Zhu, J. Y., Park, T., Isola, P., Efros, A. A.: Unpaired image-to image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision, pp. 2242–2251 (2017)

  38. Wang, L., Sindagi, V., Patel, V.: High-quality facial photo-sketch synthesis using multi-adversarial networks. In: 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pp. 83–90. IEEE, (2018)

  39. Peng, C., Wang, N., Li, J., Gao, X.: Face sketch synthesis in the wild via deep patch representation-based probabilistic graphical model. IEEE Trans. Inf. Forensics Secur. 15, 172–183 (2020)

    Google Scholar 

  40. Zhu, M., Li, J., Wang, N., Gao, X.: A deep collaborative framework for face photo-sketch synthesis. IEEE Trans. Neural Netw. Learn. Syst. 30(10), 3096–3108 (2019)

    Google Scholar 

  41. Zhang, S., Ji, R., Hu, J., Lu, X., Li, X.: Face sketch synthesis by multi-domain adversarial learning. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1419–1428 (2019)

    Google Scholar 

  42. Zhang, M., Wang, N., Li, Y., Gao, X.: Bionic face sketch generator. IEEE Trans. Cybern. 1–14, (2019)

  43. Yu, J., Xu, X., Gao, F., Shi, S., Wang, M., Tao, D., Huang, Q.: Toward realistic face photo-sketch synthesis via composition-aided GANs. IEEE Trans. Cybern. (2020)

  44. Yuen, P.C., Man, C.: Human face image searching system using sketches. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37(4), 493504 (2007)

    Google Scholar 

  45. Klare, B., Jain, A. K.: Sketch-to-photo matching: a feature-based approach. In: Biometric technology for human identification VII Vol. 7667, p. 766702, (2010)

  46. Klum, S.J., Han, H., Klare, B.F., Jain Jain, A.K.: The FaceSketchID system: matching facial composites to mugshots. IEEE Trans. Inf. Forensics Secur. 9(12), 2248–2263 (2014)

    Google Scholar 

  47. Bhatt, H.S., Bharadwaj, S., Singh, R., Vatsa, M.: Memetically optimized MCWLD for matching sketches with digital face images. IEEE Trans. Inf. Forensics Secur. 7(5), 1522–1535 (2012)

    Google Scholar 

  48. Han, H., Klare, B.F., Bonnen, K., Jain, A.K.: Matching composite sketches to face photos: a component-based approach. IEEE Trans. Inf. Forensics Secur. 8(1), 191–204 (2012)

    Google Scholar 

  49. Galea, C., Farrugia, R. A.: Face photo-sketch recognition using local and global texture descriptors. In: Signal Processing Conference (EUSIPCO), 2016 24th European, pp. 22402244, IEEE, (2016)

  50. Buoncompagni, S., Franco, A., Maio, D.: Efficient sketch recognition based on shape features and multidimensional indexing. In: International Conference on Computer Recognition Systems, pp. 159–169, Springer, (2017)

  51. Mittal, P., Jain, A., Goswami, G., Vatsa, M., Singh, R.: Composite sketch recognition using saliency and attribute feedback. Inf. Fusion 33, 86–99 (2017)

    Google Scholar 

  52. Karczmarek, P., Kiersztyn, A., Rutka, P., Pedrycz, W.: Linguistic descriptors in face recognition: a literature survey and the perspectives of future development. In: 2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 98–103, IEEE, (2015)

  53. Rahman, A., Beg, M.S.: Face sketch recognition using sketching with words. Int. J. Mach. Learn. Cybern. 6(4), 597–605 (2015)

    Google Scholar 

  54. Karczmarek, P., Kiersztyn, A., Pedrycz, W., Dolecki, M.: Linguistic descriptors in face recognition. Int. J. Fuzzy Syst. 20(8), 2668–2676 (2018)

    Google Scholar 

  55. Kazemi, H., Iranmanesh, M., Dabouei, A., Soleymani, S., Nasrabadi, N. M.: Facial attributes guided deep sketch-to-photo synthesis. In: 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 1–8. IEEE, (2018)

  56. Zhu, M., Li, J., Wang, N., Gao, X.: A deep collaborative framework for face photo–sketch synthesis. IEEE Trans. Neural Netw. Learn. Syst. 30(10), 3096–3108 (2019)

    Google Scholar 

  57. Wan, W., Lee, H. J.: Deep feature representation and ball-tree for face sketch recognition. Int. J. Syst. Assurance Eng. Manage. 1–6, (2019)

  58. Kazemi, H., Soleymani, S., Dabouei, A., Iranmanesh, M., Nasrabadi, N. M.: Attribute-centered loss for soft-biometrics guided face sketch-photo recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 499–507, (2018)

  59. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)

    Google Scholar 

  60. Galea, C., Farrugia, R.A.: Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning. IEEE Trans. Inf. Forensics Secur. 13(6), 1421–1431 (2017)

    Google Scholar 

  61. Parkhi, O.M., Andrea, V., Andrew, Z.: Deep face recognition. In: British Machine Vision Conference, 1(3), (2015)

  62. Khan, A., Jalal, A. S.: A framework for suspect face retrieval using linguistic descriptions. Expert Syst. Appl. 141(1), (2020)

  63. Van, K., Peter, J., Shara, K.L.: Portraying perpetrators; the validity of offender descriptions by witnesses. Law Hum. Behav. 21(6), 661–685 (1997)

    Google Scholar 

  64. Meissner, C. A., Sporer, S. L., Schooler, J. W.: Person descriptions as eyewitness evidence, Handbook of eyewitness psychology: Memory for people, pp. 1–34, (2017)

  65. Wu, J.K., Narasimhalu, A.D.: Fuzzy content-based retrieval in image databases. Inf Process Manag 34, 513–534 (1998)

    Google Scholar 

  66. Kumar, N., Berg, A., Belhumeur, P.N., Nayar, S.: Describable visual attributes for face verification and image search. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1962–1977 (2011)

    Google Scholar 

  67. Conilione, P., Wang, D.: Fuzzy approach for semantic face image retrieval. Comput. J. 55(9), 1130–1145 (2012)

    Google Scholar 

  68. Alattab, A. A., Kareem, S. A.: Semantic features selection and representation for facial image retrieval system. In: 4th International Conference on Intelligent Systems, Modeling and Simulation, pp. 299–304, (2013)

  69. Karczmarek, P., Pedrycz, W., Reformat, M., Akhoundi, E.: A study in facial regions saliency: a fuzzy measure approach. Soft. Comput. 18(2), 379–391 (2014)

    Google Scholar 

  70. Dolecki, M., Karczmarek, P., Kiersztyn, A., Pedrycz, W.: Face recognition by humans performed on basis of linguistic descriptors and neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 5135–5140, (2016)

  71. Kiersztyn, A., Karczmarek, P., Dolecki, M., Pedrycz, W.: Linguistic descriptors and fuzzy sets in face recognition realized by humans. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1120–1126, (2016)

  72. Karczmarek, P., Pedrycz, W., Kiersztyn, A., Rutka, P.: A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft. Comput. 21(24), 7503–7517 (2017)

    Google Scholar 

  73. Kurach, D., Rutkowska, D., Rakus-Andersson, E.: Face classification based on linguistic description of facial features. In: International Conference on Artificial Intelligence and Soft Computing (pp. 155–166, (2014)

  74. Klare, B. F., Klum, S., Klontz, J. C., Taborsky, E., Akgul, T., Jain, A. K.: Suspect identification based on descriptive facial attributes. In: IEEE International Joint Conference on Biometrics, pp. 1–8, (2014)

  75. LaVergne, D., Tiferes, J., Jenkins, M., Gross, G., Bisantz, A.: Linguistic estimations of human attributes. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 318–322, (2016)

  76. Singh, A.K., Nandi, G.C.: Visual perception-based criminal identification: a query-based approach. J. Exp. Theor. Artif. Intell. 29(1), 175–196 (2017)

    Google Scholar 

  77. Pallavi, S., Sannidhan, M.S., Sudeepa, K. B., Bhandary, A.: A novel approach for generating composite sketches from mugshot photographs. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 460–465, (2018)

  78. Bobulski, J., Kubanek, M.: Person identification system using an identikit picture of the suspect. Opt. Appl. 42(4), 865–873 (2012)

    Google Scholar 

  79. Singh, S., Sinha, M.: Forensic sketch recognition using user specific facial region. Int. J. Biometrics 8(2), 134–144 (2016)

    Google Scholar 

  80. Kukharev, G. A., Matveev, Y. N., Shchegoleva, N. L.: Matching of a sketches with an original photo. In: 2015 XVIII International Conference on Soft Computing and Measurements (SCM), pp. 157–159, (2015)

  81. Khan, M.A., Jalal, A.S.: A fuzzy rule based multimodal framework for face sketch-to-photo retrieval. Expert Syst. Appl. 134, 138–152 (2019)

    Google Scholar 

  82. Bhatt, H. S., Bharadwaj, S., Singh, R., Vatsa, M.: On matching sketches with digital face images. In: 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–7. IEEE, (2010)

  83. National Institute of Standards and Technology (NIST), NIST Special Database 32—Multiple Encounter Dataset (MEDS) 2011 [Online]. Available: http://www.nist.gov/itl/iad/ig/sd32.cfm

  84. Martinez, A. M.: The AR face database, CVC Technical Report24, (1998)

  85. Nagpal, S., Singh, M., Singh, R., Vatsa, M., Noore, A., Majumdar, A.: Face sketch matching via coupled deep transform learning. In: Proceedings of the IEEE international conference on computer vision, pp. 5419–5428, (2017)

  86. Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: Xm2vtsdb: The extended m2vts database. In: Second International Conference on Audio and Video-based Biometric Person Authentication, March (1999)

  87. Chugh, T., Singh, M., Nagpal, S., Singh, R., Vatsa, M.: Transfer learning based evolutionary algorithm for composite face sketch recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 117–125, (2017)

  88. Biometrics and identification innovation center, wvu multi-modal dataset. Available at http://biic.wvu.edu/

  89. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp. 3730–3738, (2015)

  90. Frowd, C., Hancock, P., Carson, D.: EvoFIT: a Holistic, Evolutionary facial imaging technique for creating composites. ACM Trans. Appl. Psychol. 1(1), 19–39 (2004)

    Google Scholar 

  91. Gary, B. H., Mattar, M., Lee, H., Learned-Miller, E.: Learning to align from scratch. Advances in Neural Information Processing Systems (NIPS), (2012)

  92. Sonavane, M. S.: Criminal photograph retrieval based on forensic face sketch using LFDA Framework. In: National Conference on Computational Intelligence and Deep Learning (NCCIDL-19), Vol. 5, No. 2, pp. 71–74, (2019)

  93. Wells, G., Hasel, L.: Facial composite production by eye witnesses. Current Directions Psychol. Sci. 16(1), 6–10 (2007)

    Google Scholar 

  94. Song, Y., Bao, L., Yang, Q., Yang, M.-H.: ‘Real-time exemplar based face sketch synthesis. In: Proceedings of the European Conference on Computer Vision, pp. 800–813 (2019)

  95. Wang, N., Gao, X., Li, J.: Random sampling and locality constraint for face sketch. Pattern Recognit. 76 (2017)

  96. Zhang, S., Gao, X., Wang, N., Li, J., Zhang, M.: Face sketch synthesis via sparse representation-based greedy search. IEEE Trans Image Process 24(8), 2466–2477 (2015)

    MathSciNet  MATH  Google Scholar 

  97. Chang, M., Zhou, L., Han, Y., Deng, X.: Face sketch synthesis via sparse representation. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2146–2149, (2010)

  98. Gao, X., Wang, N., Tao, D., Li, X.: ‘Face sketch–photo synthesis and retrieval using sparse representation.’ IEEE Trans. Circuits Syst. Video Technol. 22(8), 1213–1226 (2012)

    Google Scholar 

  99. Zhou, H., Kuang, Z., Wong, K.Y.K.: Markov weight fields for face sketch synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1097, (2012)

  100. Chang, L., Zhou, M., Deng, X., Wu, Z., Han, Y. : Face sketch synthesis via multivariate output regression. In: International Conference on Human-Computer Interaction, pp. 555–561. Springer, Berlin, Heidelberg, (2011)

  101. Zhang, J., Wang, N., Gao, X., Tao, D., Li, X.: Face sketch-photo synthesis based on support vector regression. In 2011 18th IEEE International Conference on Image Processing, pp. 1125–1128. IEEE, (2011)

  102. Wang, N., Tao, D., Gao, X., Li, X., Li, J.: Transductive face sketch photo synthesis. IEEE Trans. Neural Netw. Learn. Syst. 24(9), 1364–1376 (2013)

    Google Scholar 

  103. Zhu, M., Wang, N.: A simple and fast method for face sketch synthesis. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 168–171. (2016)

  104. Peng, C., Gao, X., Wang, N., Tao, D., Li, X., Li, J.: Multiple representations-based face sketch–photo synthesis. IEEE Trans. Neural Netw. Learn. Syst. 27(11), 2201–2215 (2016)

    Google Scholar 

  105. Jiao, L., Zhang, S., Li, L., Liu, F., Ma, W.: A modified convolutional neural network for face sketch synthesis. Pattern Recogn. 76, 125–136 (2018)

    Google Scholar 

  106. Hu, M., Guo, J.: Facial attribute-controlled sketch-to-image translation with generative adversarial networks. EURASIP J. Image Video Process. 1, 1–13 (2020)

    Google Scholar 

  107. Ouyang, S., Hospedales, T., Song, Y.Z., Li, X., Loy, C.C., Wang, X.: A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution. Image Vis. Comput. 56, 28–48 (2016)

    Google Scholar 

  108. Khan, Z., Hu, Y., Mian, A.: Facial self-similarity for sketch to photo matching. In: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1–7. IEEE, (2012)

  109. Galoogahi, H., Sim, T.: Face photo retrieval by sketch example. In: Proceedings of the 20th ACM international conference on multimedia, pp. 949–952. (2012)

  110. Galoogahi, H., Sim, T.: Inter-modality face sketch recognition. In: IEEE International Conference on Multimedia and Expo, pp. 224–229. IEEE, (2012)

  111. Galoogahi, H., Sim, T.: Face sketch recognition by local randon binary pattern: LRBP. In: Processing of 19th IEEE International Conference on Image, pp. 1837–1840. IEEE, (2012)

  112. Pramanik, S., Bhattacharjee, D.: Geometric feature based face-sketch recognition. In: International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012), pp. 409–415. IEEE, (2012)

  113. Klare, B., Li, Z., Jain, A.K.: Matching forensic sketches to mugshot photos. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 639–646 (2010)

    Google Scholar 

  114. Zhang, Y., McCullough, C., Sullins, J., Ross, C.: Human and computer evaluations of face sketches with implications for forensic investigations. In: Proceedings of the International Conference on Biometrics: Theory, Applications and Systems, pp. 1–7, (2008)

  115. Zhang, Y., McCullough, C., Sullins, J., Ross, C.: Hand-drawn face sketch recognition by humans and a PCA-based algorithm for forensic applications. IEEE Trans. Syst. Man Cybern. A 40(3), 475–485 (2010)

    Google Scholar 

  116. Setumin, S., Suandi, S.A.: Difference of Gaussian oriented gradient histogram for face sketch to photo matching. IEEE Access 6, 39344–39352 (2018)

    Google Scholar 

  117. Gong, D., Li, Z., Liu, J., Qiao, Y.: Multi-feature canonical correlation analysis for face photo-sketch image retrieval. In: Proceedings of the 21st ACM international conference on Multimedia, pp. 617–620, (2013)

  118. Roy, H., Bhattacharjee, D.: Face sketch-photo recognition using local gradient checksum: LGCS. Int. J. Mach. Learn. Cybern. 8(5), 1457–1469 (2017)

    Google Scholar 

  119. Liu, D., Li, J., Wang, N., Peng, C., Gao, X.: Composite components-based face sketch recognition. Neurocomputing 302, 46–54 (2018)

    Google Scholar 

  120. Radman, A., Suandi, S.A.: Robust face pseudo-sketch synthesis and recognition using morphological-arithmetic operations and HOG-PCA. Multimedia Tools Appl. 77(19), 25311–25332 (2018)

    Google Scholar 

  121. Setumin, S., Suandi, S.A.: Cascaded static and dynamic local feature extractions for face sketch to photo matching. IEEE Access 7, 27135–27145 (2019)

    Google Scholar 

  122. Dalal, S., Vishwakarma, V.P., Kumar, S.: Feature-based sketch-photo matching for face recognition. Procedia Comput. Sci. 167, 562–570 (2020)

    Google Scholar 

  123. Wan, W., Lee, H.J.: Deep feature representation for face sketch recognition. Adv. Sci. Technol. Eng. Syst. J. (ASTESJ) 4(2), 107–111 (2019)

    Google Scholar 

  124. Iranmanesh, S. M., Kazemi, H., Soleymani, S., Dabouei, A., Nasrabadi, N. M.: Deep sketch-photo face recognition assisted by facial attributes. In: IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–10, (2018)

  125. Mittal, P., Vatsa, M., Singh, R.: Composite sketch recognition via deep network-a transfer learning approach. In: 2015 International Conference on Biometrics (ICB), pp. 251–256, (2015)

  126. Khan, M. A., Jalal, A. S.: Suspect identification using local facial attributed by fusing facial landmarks on the forensic sketch. In: International Conference on Contemporary Computing and Applications (IC3A), pp. 181–186, (2020)

  127. Galea, C., Farrugia, R.A.: Forensic face photo-sketch recognition using a deep learning-based architecture. IEEE Signal Process. Lett. 24(11), 1586–1590 (2017)

    Google Scholar 

  128. Galea, C., Farrugia, R. A.: A large-scale software-generated face composite sketch database. In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–5, (2016)

  129. Kukharev, G.A., Matveev, Y.N., Shchegoleva, N.L.: New solutions for face photo retrieval based on sketches. Pattern Recognit. Image Anal. 26(1), 165–175 (2016)

    Google Scholar 

  130. Li, X., Cao, X.: A simple framework for face photo-sketch synthesis. Math. Problems Eng. (2012)

  131. Zhang, L., Lin, L., Wu, X., Ding, S., Zhang, L.: End-to-end photo-sketch generation via fully convolutional representation learning. In: Proceedings of the 5th ACM International Conference on Multimedia Retrieval, Jun. 2015, pp. 627–634, (2015)

  132. Fg-net aging database - http://www.fgnet.rsunit.com/

  133. Martinho-Corbishley, D., Nixon, M. S., Carter, J. N. : Soft biometric retrieval to describe and identify surveillance images. In: 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1–6, (2016)

  134. Wang, N., Gao, X., Sun, L., Li, J.: Bayesian face sketch synthesis. IEEE Trans. Image Process. 26(3), 1264–1274 (2017)

    MathSciNet  MATH  Google Scholar 

  135. Gibson, L.: Forensic Art Essentials. Elsevier, Amsterdam (2008)

    Google Scholar 

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Acknowledgements

The authors highly acknowledge Ministry of Electronics and Information Technology, Government of India, for its fund Grant Approval No. 4(8)/2020-ITEA.

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  1. Department of Computer Engineering and Applications, GLA University, Mathura, UP, 281406, India

    Anand Singh Jalal, Dilip Kumar Sharma & Bilal Sikander

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  1. Anand Singh Jalal
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  2. Dilip Kumar Sharma
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Jalal, A.S., Sharma, D.K. & Sikander, B. Suspect face retrieval using visual and linguistic information. Vis Comput 39, 2609–2635 (2023). https://doi.org/10.1007/s00371-022-02482-6

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