Abstract
The rapid enhancement in the development of information technology has driven the development of human facial image recognition. Recently, facial recognition has been successfully applied in several distinct domains with the help of computing and information technology. This kind of application plays a significant role in the process of digital forensics investigation, recognizing the patterns of a human face based on the partial matching of images that would be in 24-bit color image format, including the spacing of the eyes, the bridging of the nose, the contour of the lips, ears, and chin. In this paper, we have proposed and implemented an image recognition model based on principal component analysis, genetic algorithms, and neural networks, in which PCA reduces the dimension of the benchmark dataset, while genetic algorithms and neural nets optimize the searching patterns of image matching and provide highly efficient output with a minimal amount of time. Through the experiment results on the human facial images dataset of the Georgia Institute of Technology, the overall match showed that the proposed model can achieve the recognition of human face images with an accuracy rate of 93.7%. Moreover, this model helps to examine, analyze, and detect individuals by partial matching with reidentification in the procedure of forensics investigation. The experimental result shows the robustness of the proposed model in terms of efficiency compared to other state-of-the-art methods.
Similar content being viewed by others
References
Abdullah NA, Saidi MJ, Rahmi NHA, Wen CC, Hamid IRA (2017) Face recognition for criminal identification: An implementation of principal component analysis for face recognition. In: AIP Conference Proceedings, vol 1891, no 1. AIP Publishing LLC, Melville, p 020002
Alsmadi MK, Hamed AY, Badawi UA, Almarashdeh I, Salah A, Farag TH, Hassan W, Jaradat G, Alomari YM, Alsmadi HM (2017) Face image recognition based on partial face matching using genetic algorithm
Årnes A (ed) (2017) Digital forensics. Wiley, Hoboken
Bastanfard A, Nik MA, Dehshibi MM (2007) Iranian face database with age, pose and expression. In: 2007 International Conference on Machine Vision. IEEE, pp 50-55
Bhatele K, Raj S, Jain A, Kataria, Jain P (2020) The fundamentals of digital forensics. Handbook of Research on Multimedia Cyber Security. IGI Global, pp 165–175
Bowyer KW, Burge MJ (eds) (2016) Handbookof iris recognition. Springer, London
Casey E (2009) Handbook of digital forensics and investigation. Academic, Cambridge
Chelali FZ, Djeradi A (2014) Face recognition system using neural network with Gabor and discrete wavelet transform parameterization. In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, pp 17-24
Darwin C (2018) On the origin of species: or; the preservation of the favoured races in the struggle for life. Read Books Ltd
Gilani SZ, Mian A, Eastwood P (2017) Deep, dense and accurate 3D face correspondence for generating population specific deformable models. Pattern Recogn 69:238–250
Greiff S, Fischer A, Stadler M, Wüstenberg S (2015) Assessing complex problem-solving skills with multiple complex systems. Think Reason 21(3):356–382
Gupta P, Tiwari K, Arora G (2019) Fingerprint indexing schemes–A survey. Neurocomputing 335:352–365
Jones G, Maria, Godfrey Winster S (2017) Forensics analysis on smart phones using mobile forensics tools. Int J Comput Intell Res 13(8):1859–1869
Jordan MI, Tom M, Mitchell (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260
Joshi JC, Gupta KK (2016) Face recognition technology: a review. IUP J Telecommun 8(1):53
Kasar MM, Bhattacharyya D, Kim TH (2016) Face recognition using neural network: a review. Int J Secur Its Appl 10(3):81–100
Kent K, Chevalier S, Grance T, Dang H (2006) Sp 800-86. guide to integrating forensic techniques into incident response
Khan AA, Laghari AA, Awan SA (2021) Machine learning in computer vision: a review. EAI Transactions on Scalable Information Systems (2021):e4
Khan AA, Shaikh ZA, Laghari AA, Bourouis S, Wagan AA, Ali GAAA (2021) Blockchain-aware distributed dynamic monitoring: a smart contract for fog-based drone management in land surface changes. Atmosphere 12(11):1525
Khan AA, Uddin M, Shaikh A, Laghari AA, Rajput A (2021) MF-Ledger: blockchain hyperledger sawtooth-enabled novel and secure multimedia chain of custody forensic investigation architecture. IEEE Access
Khan AA, Laghari AA, Shaikh AA, Dootio MA, Estrela VV, Lopes RT (2021) A blockchain security module for Brain-Computer Interface (BCI) with Multimedia Life Cycle Framework (MLCF). Neurosci Inform :100030
Khan AA, Shaikh ZA, Baitenova L, Mutaliyeva L, Moiseev N, Mikhaylov A, Laghari AA, Idris SA, Alshazly H (2021) QoS-ledger: smart contracts and metaheuristic for secure quality-of-service and cost-efficient scheduling of medical-data processing. Electronics 10(24):3083
Khan AA, Shaikh AA, Cheikhrouhou O, Laghari AA, Rashid M, Shafiq M, Hamam H (2021) IMG-forensics: Multimedia‐enabled information hiding investigation using convolutional neural network. IET Image Processing
Laghari AA, Wu K, Laghari RA, Ali M, Khan AA (2021) A review and state of art of Internet of Things (IoT). Arch Comput Methods Eng :1–19
Li M, Yu X, Ryu KH, Lee S, Theera-Umpon N (2018) Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition. Cluster Comput 21(1):1117–1126
Li L, Mu X, Li S, Peng H (2020) A review of face recognition technology. IEEE Access 8:139110–139120
Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and dense convolutional network. J Real-Time Image Proc 17(1):125–135
Mahmud F, Haque ME, Zuhori ST, Pal B (2014) Human face recognition using PCA based Genetic Algorithm. In 2014 International Conference on Electrical Engineering and Information & Communication Technology. IEEE, pp 1-5
Mehraj H, Mir AH (2020) Feature vector extraction and optimisation for multimodal biometrics employing face, ear and gait utilising artificial neural networks. Int J Cloud Comput 9:2–3
Mitchell RS, Michalski JG (2013) Carbonell. An artificial intelligence approach. Springer, Berlin
Mousavi SMH, Mirinezhad SY (2021) Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v. 2 sensor. SN Appl Sci 3(1):1–17
Narayanan NK, Kabeer V (2010) Face recognition using nonlinear feature parameter and artificial neural network. Int J Comput Intell Syst 3(5):566–574
Okada K (2011) High-speed image matching using partial template consisting of multiple rectangular areas extracted by genetic algorithm. Electron Commun Jpn 94(10):1–9
Pal A, Yogendra Narain Singh (2018) ECG Biometric recognition. In: International Conference on Mathematics and Computing. Springer, Singapore, pp 61-73
Roetzela W, Luob X, Chenc D (2019) Optimal control process of heat exchanger networks. Design Operation of Heat Exchangers their Networks :431
Sabhanayagam T, Prasanna Venkatesan V, Senthamaraikannan K (2018) A comprehensive survey on various biometric systems. Int J Appl Eng Res 13(5):2276–2297
Sadler M, Regan N (2019) Game Changer. AlphaZero’s Groundbreaking Chess Strategies and the Promise of AI. Alkmaar. The Netherlands. New in Chess
Shah NF (2019) An improved framework for human face recognition. Recent findings in intelligent computing techniques. Springer, Singapore, pp 175–180
Su Y (2018) Robust video face recognition under pose variation. Neural Process Lett 47(1):277–291
Sukhija P, Behal S, Singh P (2016) Face recognition system using genetic algorithm. Procedia Comput Sci 85:410–417
Zhang Y, Xiao X, Yang L-X, Xiang Y, Zhong S (2019) Secure and efficient outsourcing of PCA-based face recognition. IEEE Trans Inf Forensics Secur 15:1683–1695
Zhi H, Liu S (2019) Face recognition based on genetic algorithm. J Vis Commun Image Represent 58:495–502
Zhong D, Du X, Zhong K (2019) Decade progress of palmprint recognition: A brief survey. Neurocomputing 328:16–28
Zhong Y, Wang X, Zhang S (2020) Robust partial matching for person search in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6827-6835
Zhou Z, Wu QJ, Wan S, Sun W, Sun X (2020) Integrating SIFT and CNN feature matching for partial-duplicate image detection. IEEE Trans Emerg Top Comput Intell 4(5):593–604
Zhou Z, Lin K, Cao Y, Yang CN, Liu Y (2020) Near-duplicate image detection system using coarse-to-fine matching scheme based on global and local CNN features. Mathematics 8(4):644
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khan, A.A., Shaikh, A.A., Shaikh, Z.A. et al. IPM-Model: AI and metaheuristic-enabled face recognition using image partial matching for multimedia forensics investigation with genetic algorithm. Multimed Tools Appl 81, 23533–23549 (2022). https://doi.org/10.1007/s11042-022-12398-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12398-x