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Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks

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Abstract

Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods.

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References

  1. Abuzneid MA, Mahmood A (2018) Enhanced human face recognition using LBPH descriptor, multi-KNN, and back-propagation neural network. IEEE Access 6:20641–20651

    Google Scholar 

  2. Agarwal V, Bhanot S (2018) Radial basis function neural network-based face recognition using firefly algorithm. Neural Comput & Applic 30(8):2643–2660

    Google Scholar 

  3. Anggo M, Arapu L (2018) Face recognition using fisherface method. J Phys Conf Ser 1028(1):012119 IOP Publishing

    Google Scholar 

  4. Arivazhagan S, Priyadharshini RA, Sowmiya S (2014) Face recognition based on local directional number pattern and ANFIS classifier. In: Int. Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp 1627–1631

  5. Aslan MS, Hailat Z, Alafif TK, Chen XW (2017) Multi-channel multi-model feature learning for face recognition. Elsevier, Pattern Recognit Lett 85:79–83

    Google Scholar 

  6. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Google Scholar 

  7. Çevik HH, Acar YE, Çunkaş M (2018) Day ahead wind power forecasting using complex valued neural network. In: 2018 International Conference on Smart Energy Systems and Technologies (SEST). IEEE. pp 1–6

  8. Chen XW, Aslan M, Zhang K, Huang T (2015) Learning multi-channel deep feature representations for face recognition. In: Feature Extraction: Modern Questions and Challenges, pp 60–71

  9. Chen Z, Wu XJ, Xu T, Kittler J (2021) Learning alternating deep-layer cascaded representation. IEEE Signal Processing Letters 28:1520–1524

    Google Scholar 

  10. Chen Z, Wu XJ, Xu T, Kittler J (2022) Discriminative dictionary pair learning with scale-constrained structured representation for image classification. IEEE Trans Neural Netw Learn Syst PP:1–15

    Google Scholar 

  11. Chiachia G, Falcao AX, Pinto N, Rocha A, Cox D (2014) Learning person-specific representations from faces in the wild. IEEE Trans Inf Forensics Secur 9(12):2089–2099

    Google Scholar 

  12. Choudhury ZH (2019) Biometrics passport authentication using facial Marks.

  13. Choudhury ZH, Rabbani MMA (2021) Facial blemishes detection and encryption with secure force algorithm into HCC2D code for biometric-passport. Inf Secur J: A Global Perspective 30(6):342–358

    Google Scholar 

  14. Cui Y, Yi Z, Duan J, Shi D, Wang Z (2019) A Rprop-neural-network-based PV maximum power point tracking algorithm with short-circuit current limitation. In: 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE. pp 1–5

  15. Dadi HS, Pillutla GKM, Makkena ML (2018) Face recognition and human tracking using GMM, HOG and SVM in surveillance videos. Ann Data Sci 5(2):157–179

    Google Scholar 

  16. Dash R (2018) Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction. Appl Soft Comput 67:215–231

    Google Scholar 

  17. Egrioglu E, Yolcu U, Bas E (2019) Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granular Computing 4(4):639–654

    Google Scholar 

  18. Er MJ, Wu S, Lu J, Toh HL (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13(3):697–710

    Google Scholar 

  19. Ergul E (2017) Relative attribute based incremental learning for image recognition. CAAI Trans Intell Technol 2(1):1–11

    Google Scholar 

  20. Everitt BS, Dunn G (2001) Applied multivariate data analysis, vol 2. Arnold, London

    Google Scholar 

  21. Fan F, Wang G (2020) Fuzzy logic interpretation of quadratic networks. Neurocomputing 374:10–21

    Google Scholar 

  22. Fan F, Shan H, Kalra MK, Singh R, Qian G, Getzin M, … Wang G (2019) Quadratic autoencoder (Q-AE) for low-dose CT denoising. IEEE Trans Med Imaging 39(6):2035–2050

    Google Scholar 

  23. Fan F, Xiong J, Wang G (2020) Universal approximation with quadratic deep networks. Neural Netw 124:383–392

    Google Scholar 

  24. Faraji MR, Qi X (2018) Face recognition under varying illuminations with multi-scale gradient maximum response. Neurocomputing 308:87–100

    Google Scholar 

  25. Fawwad Hussain M, Wang H, Santosh KC (2018) Gray level face recognition using spatial features. In: International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer, Singapore, pp 216–229

  26. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Hum Genet 7(2):179–188

    Google Scholar 

  27. Fontaine X, Achanta R, Süsstrunk S (2017) Face recognition in real-world images. In: IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1482–1486

  28. Gao J, Deng B, Qin Y, Wang H, Li X (2018) Enhanced radar imaging using a complex-valued convolutional neural network. IEEE Geosci Remote Sens Lett 16(1):35–39

    Google Scholar 

  29. Gautam A, Bhateja V, Tiwari A, Satapathy SC (2018) An improved mammogram classification approach using back propagation neural network. In: Data Engineering and Intelligent Computing. Springer, Singapore, pp 369–376

    Google Scholar 

  30. Görgel P, Simsek A (2019) Face recognition via deep stacked denoising sparse autoencoders (DSDSA). Appl Math Comput 355:325–342

    MathSciNet  Google Scholar 

  31. Gupta M (2021) An efficacious method for face recognition using DCT and neural network. In: Sustainable communication networks and application. Springer, Singapore, pp 671–683

    Google Scholar 

  32. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (1998) Multivariate data analysis, Upper Saddle River: Prentice hall, 5(3): 207–219

    Google Scholar 

  33. Hataya R, Zdenek J, Yoshizoe K, Nakayama H (2020) Faster autoaugment: Learning augmentation strategies using backpropagation. In: European Conference on Computer Vision. Springer, Cham, pp 1–16

  34. Hathaliya JJ, Tanwar S, Evans R (2020) Securing electronic healthcare records: A mobile-based biometric authentication approach. J Inf Secur Appl 53:102528

    Google Scholar 

  35. He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Google Scholar 

  36. Hermanto RPS, Nugroho A (2018) Waiting-time estimation in bank customer queues using RPROP neural networks. Procedia Comput Sci 135:35–42

    Google Scholar 

  37. Jain V (2002) The Indian face database. http://vis-www.cs.Umass.edu/vidit/IndianFaceDatabase. Accessed 10 Jan 2022

  38. Javed Mehedi Shamrat FM, Ghosh P, Tasnim Z, Khan AA, Uddin M, Chowdhury TR (2022) Human face recognition using eigenface, SURF method. In: Pervasive Computing and Social Networking. Springer, Singapore, pp 73–88

    Google Scholar 

  39. Jia L, Yang B, Zhang W (2018) Research on stock forecasting based on GPU and complex-valued neural network. In: International Conference on Intelligent Computing. Springer, Cham, pp 120–128

    Google Scholar 

  40. Johansson M (1999) The hilbert transform. Mathematics Master’s Thesis. Växjö University, Suecia. Disponible en internet: http://w3.msi.vxu.se/exarb/mj_ex.pdf, consultadoel, 9. Accessed 18 Jan 2022

  41. Kakkad V, Patel M, Shah M (2019) Biometric authentication and image encryption for image security in cloud framework. Multiscale Multidiscip Model Exp Des 2(4):233–248

    Google Scholar 

  42. Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Exp Syst 1(4):111–122

    Google Scholar 

  43. Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80(5):8091–8126

    Google Scholar 

  44. Kumaar S, Vishwanath RM, Omkar SN, Majeedi A, Dogra A (2018) Disguised facial recognition using neural networks. In: 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP). IEEE. pp 28–32

  45. Li L, Wang Z, Li Y, Shen H, Lu J (2018) Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays. Appl Math Comput 330:152–169

    MathSciNet  Google Scholar 

  46. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

    Google Scholar 

  47. Lukas S, Mitra AR, Desanti RI, Krisnadi D (2016) Student attendance system in classroom using face recognition technique. In: IEEE Int. Conference on Information and Communication Technology Convergence (ICTC), pp 1032–1035

  48. Mantoro T, Ayu MA (2018) Multi-faces recognition process using Haar cascades and eigenface methods. In: 2018 6th International Conference on Multimedia Computing and Systems (ICMCS). IEEE. pp 1–5

  49. Martinez AM (1998) The AR face database. CVC Technical Report24

  50. Nemati S, Rohani R, Basiri ME, Abdar M, Yen NY, Makarenkov V (2019) A hybrid latent space data fusion method for multimodal emotion recognition. IEEE Access 7:172948–172964

    Google Scholar 

  51. Oyama K, Hirose A (2018) Performance of entire-spectrum-processing complex-valued neural-network filter to generate digital elevation model in interferometric radar. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE. pp 1–7

  52. Pinto N, Stone Z, Zickler T, Cox D (2011) Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 35–42

  53. Rajasekar V, Predić B, Saracevic M, Elhoseny M, Karabasevic D, Stanujkic D, Jayapaul P (2022) Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm. Sci Rep 12(1):1–11

    Google Scholar 

  54. Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78(16):22691–22710

    Google Scholar 

  55. Roh SB, Oh SK, Yoon JH, Seo K (2019) Design of face recognition system based on fuzzy transform and radial basis function neural networks. Soft Comput 23(13):4969–4985

    Google Scholar 

  56. Sardar A, Umer S, Rout RK, Wang SH, Tanveer M (2023) A secure face recognition for IoT-enabled healthcare system. ACM Trans Sensor Netw 19(3):1–23

    Google Scholar 

  57. Sasirekha K, Thangavel K (2019) Optimization of K-nearest neighbor using particle swarm optimization for face recognition. Neural Comput & Applic 31(11):7935–7944

    Google Scholar 

  58. Seha SNA, Hatzinakos D (2020) EEG-based human recognition using steady-state AEPs and subject-unique spatial filters. IEEE Trans Inf Forensics Secur 15:3901–3910

    Google Scholar 

  59. Sharma S, Bhatt M, Sharma P (2020) Face recognition system using machine learning algorithm. In: 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE. pp 1162–1168

  60. Shi B, Bai X, Yao C (2017) An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans Pattern Anal Mach Intell 39(11):2298–2304

    Google Scholar 

  61. Srivastava S, Agarwal S (2011) Offline signature verification using grid based feature extraction. In: IEEE Int. Conference on Computer and Communication Technology (ICCCT), pp 185–190

  62. Srivastava S, Tripathi B (2018) On the Deep Hybrid Computational Model for Face Recognition. Int J Intell Eng Syst 11(1):121–130

    Google Scholar 

  63. Tabachnick BG, Fidell LS (2007) Using multivariate statistics. Allyn and Bacon/Pearson Education

    Google Scholar 

  64. Tripathi BK (2017) On the complex domain deep machine learning for face recognition. Appl Intell 47(2):382–396

    Google Scholar 

  65. Tripathi BK, Kalra PK (2011) On efficient learning machine with root-power mean neuron in complex domain. IEEE Trans Neural Netw 22(5):727–738

    Google Scholar 

  66. Tuncer T, Dogan S, Akbal E (2019) Discrete complex fuzzy transform based face image recognition method. Int J Image Graph Signal Process (IJIGSP) 11(4):1–7

    Google Scholar 

  67. Venkatachalam K, Prabu P, Almutairi A, Abouhawwash M (2021) Secure biometric authentication with de-duplication on distributed cloud storage. PeerJ Computer Science 7:e569

    Google Scholar 

  68. Virtue P, Stella XY, Lustig M (2017) Better than real: complex-valued neural nets for MRI fingerprinting. In: 2017 IEEE international conference on image processing (ICIP). IEEE. pp 3953–3957

  69. Welling M (2005) Fisher linear discriminant analysis. Department of Computer Science, University of Toronto, 3(1)

  70. Xu Z, Xiong J, Yu F, Chen X (2020) Efficient Neural Network Implementation with Quadratic Neuron. arXiv preprint arXiv:2011.10813

  71. Zhang C, Li H, Chen C, Qian Y, Zhou X (2020) Enhanced group sparse regularized nonconvex regression for face recognition. IEEE Trans Pattern Anal Mach Intell 44(5):2438–2452

    Google Scholar 

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

    Swati Srivastava & Himanshu Sharma

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  1. Swati Srivastava
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Srivastava, S., Sharma, H. Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks. Multimed Tools Appl 83, 14085–14109 (2024). https://doi.org/10.1007/s11042-023-16274-0

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