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Gender prediction system through behavioral biometric handwriting: a comprehensive review

  • Data analytics and machine learning
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Abstract

Identification of a person on the basis of different characteristics is a prevailing area of research. Both behavioral biometrics and physical biometrics are used as measures to recognize a person’s identity. Physical biometrics includes fingerprints and IRIS patterns; whereas behavioral biometrics includes some sort of pattern in human activities like handwriting. Handwriting, like other biometrics, is one of the best attributes for implicitly identifying a person. Every person has a different style of handwriting. Gender prediction on the basis of handwriting styles in different Indian and non-Indian scripts offers a vast area for research and is an effective strategy for biometrics. This paper's major goal is to give an in-depth analysis of gender prediction using handwriting in non-Indic and Indic scripts. The intention is to provide a variety of feature extraction methods, datasets available, and a taxonomy of conventional and machine learning-based tools for gender prediction on the basis of handwriting. This article discusses the context, survey protocol, methodology, and various datasets used by the various researchers. The compiled study used for feature extraction and classification methods, along with a critical analysis of the work done, is also elaborated in this manuscript.

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References

  • Ahmed AA, Sulong G (2014) Arabic writer identification: a review of literature. J Theor Appl Inf Technol 69:474–484

    Google Scholar 

  • Ahmed M, Rasool AG, Afzal H, Siddiqi I (2017a) Improving handwriting-based gender classification using ensemble classifiers. Expert Syst Appl 85:158–168. https://doi.org/10.1016/j.eswa.2017.05.033

    Article  Google Scholar 

  • Ahmed AA, Hasan H, Hameed FA, Al-Sanjary OI (2017b) Writer identification on multi-script handwritten using optimum features. Science 2:178–185

    Google Scholar 

  • Akbari Y, Nouri K, Sadri J, Djeddi C, Siddiqi I (2017) Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata. Image vis Comput 59:17–30. https://doi.org/10.1016/j.imavis.2016.11.017

    Article  Google Scholar 

  • Al Maadeed S, Ayouby W, Hassaïıne A, Aljaam JM (2012) QUWI: An arabic and english handwriting dataset for offline writer identification. In: Frontiers in handwriting recognition (ICFHR), international conference on, pp 746–751. IEEE

  • Al Maadeed S, Hassaine A (2014) Automatic prediction of age, gender, and nationality in offline handwriting. Eurasip J Image Video Proc 2014:1–10. https://doi.org/10.1186/1687-5281-2014-10

    Article  Google Scholar 

  • Beech J, Mackintosh IC (2005) Do differences in sex hormones affect handwriting style? Evidence from digit ratio and sex role identity as determinants of the sex of handwriting. Personality Individ Differ 39:459–468

    Article  Google Scholar 

  • Bi N, Suen CY, Nobile N, Tan J (2019) A multi-feature selection approach for gender identification of handwriting based on kernel mutual information. Pattern Recogn Lett 121:123–132. https://doi.org/10.1016/j.patrec.2018.05.005

    Article  Google Scholar 

  • Bouadjenek N, Nemmour H, Chibani Y (2016) Writer’s gender classification using HOG and LBP features. Lecture Notes Electr Eng 411:317–325. https://doi.org/10.1007/978-3-319-48929-2_24

    Article  Google Scholar 

  • Bouadjenek N, Nemmour H, Chibani Y (2016b) Robust soft-biometrics prediction from off-line handwriting analysis. Appl Soft Comput 46:980–990

    Article  Google Scholar 

  • Bouadjenek N, Nemmour H, Chibani Y (2017) Fuzzy integrals for combining multiple SVM and histogram features for writer’s gender prediction. IET Biometrics 6(6):429–437. https://doi.org/10.1049/iet-bmt.2016.0140

    Article  Google Scholar 

  • Bouadjenek, N, Nemmour H, Chibani Y (2014) Local descriptors to improve off-line handwriting-based gender prediction. In: 6th international conference on soft computing and pattern recognition, SoCPaR. pp 43–47. https://doi.org/10.1109/SOCPAR.2014

  • Bouadjenek N, Nemmour H, Chibani Y (2015a) Histogram of Oriented Gradients for writer’s gender, handedness and age prediction. In: INISTA 2015a - 2015a international symposium on innovations in intelligent systems and applications, proceedings, pp 3–7. https://doi.org/10.1109/INISTA.2015.7276752

  • Bouadjenek N, Nemmour H, Chibani Y (2015b). Age, gender and handedness prediction from handwriting using gradient features. In: 2015b 13th international conference on document analysis and recognition (ICDAR), pp 1116–1120

  • Brink A, Niels R, van Batenburg R, van Den Heuvel C, Schomaker L (2010) Towards robust writer verification by correcting unnatural slant. Pattern Recognit Lett 32(3):449–457

    Article  Google Scholar 

  • Cao W, Xie Z, Zhou X, Xu Z, Zhou C, Theodoropoulos G, Wang Q (2020) A learning framework for intelligent selection of software verification algorithms. J Artif Intell 2(4):177

    Article  Google Scholar 

  • Cao W, Xie Z, Li J, Xu Z, Ming Z, Wang X (2021) Bidirectional stochastic configuration network for regression problems. Neural Netw 140:237–246

    Article  Google Scholar 

  • Chambers J, Yan W, Garhwal A, Kankanhalli M (2015) Currency security and forensics: a survey. Multimedia Tools and Applications 74(11):4013–4043

    Article  Google Scholar 

  • Dargan S, Kumar M (2019) Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey. Archiv of Comput Methods Eng 26(4):1283–1311. https://doi.org/10.1007/s11831-018-9278-z

    Article  Google Scholar 

  • Dargan S, Kumar M, Garg A, Thakur K (2020) Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM. Soft Comput 24:10111–10122

    Article  Google Scholar 

  • Djeddi C, Madeed SA, Gattal A, Saddiqi I, Meslati LS, Abed HE (2016) ICFHR 2016 competition on multi-script writer demographics classification using ‘QUWI’ database. ICFHR, Hyderabad, pp 602–606

    Google Scholar 

  • Djeddi C, Gattal A, Souici-Meslati L et al. (2014) ‘LAMIS-MSHD: a multiscript offline handwriting database’. In: Proc. Int. Conf. Frontiers in Handwriting Recognition (ICFHR), Heraklion, Greece, September, pp 93–97 DOI: https://doi.org/10.1109/ICFHR.2014.23

  • Khalid S, Naqvi U, Siddiqi I (2015) Framework for human identification through offline handwritten documents. In: 2015 international conference on computer, communications, and control technology (I4CT), Kuching, Malaysia, pp 54–58, doi: https://doi.org/10.1109/I4CT.2015.7219536

  • Dargan S, Kumar M (2021) gender classification and writer identification system based on handwriting in gurumukhi script. In: 2021 international conference on computing, communication, and intelligent systems (ICCCIS), Greater Noida, India, pp 388–393, doi: https://doi.org/10.1109/ICCCIS51004.2021.9397201

  • Fiel S, Sablatnig R (2015) writer identification and retrieval using a convolutional neural network. In: Azzopardi G, Petkov N (eds) Computer analysis of images and patterns CAIP 2015. Lecture notes in computer science. Springer, Cham

    Google Scholar 

  • Gattal A, Djeddi C, Siddiqi I, Chibani Y (2018) Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs). Expert Syst Appl 99:155–167. https://doi.org/10.1016/j.eswa.2018.01.038

    Article  Google Scholar 

  • Gattal A, Djeddi C, Bensefia A, Ennaji A (2020) Handwriting based gender classification using cold and hinge features. Image and Signal Processing: 9th International Conference, ICISP 2020, Marrakesh, Morocco, June 4–6, 2020, Proceedings. Springer International Publishing, Cham, pp 233–242. https://doi.org/10.1007/978-3-030-51935-3_25

    Chapter  Google Scholar 

  • Gideon SJ, Kandulna A, Kujur AA, Diana A, Raimond K (2018) Handwritten signature forgery detection using convolutional neural networks. Procedia Comput Sci 143:978–987. https://doi.org/10.1016/j.procs.2018.10.336

    Article  Google Scholar 

  • Grosicki E, Carre M, Brodin JM, Geoffrois E (2008) Rimes evaluation campaign for handwritten mail processing. 11th International Conference on Frontiers in Handwriting Recognition. Concordia University, Concordia, p 16

    Google Scholar 

  • Guerbai Y, Chibani Y, Hadjadji B (2017) Handwriting gender recognition system based on the one-class support vector machines. In: proceedings of the 7th international conference on image processing theory, tools and applications, IPTA 2017, 2018-Janua (4), pp 1–5. https://doi.org/10.1109/IPTA.2017.8310136

  • Gupta S, Kumar M (2020) Forensic document examination system using boosting and bagging methodologies. Soft Comput 24:5409–5426. https://doi.org/10.1007/s00500-019-04297-5

    Article  Google Scholar 

  • Guyon I, Schomaker L, Plamondon R, Liberman M, Janet S (1994) UNIPEN project of on-line data exchange and recognizer benchmarks. 12th IAPR International Conference on Pattern Recognition. IEEE Computing Society Press, Jerusalem, pp 29–33

    Google Scholar 

  • Hallale SB, Salunke G (2013) twelve directional feature extraction for handwritten English character recognition. In: 2013 international journal of recent technology and engineering (IJRTE), pp 39-42

  • Harrison D, Burkes TM, Seiger DP (2009) Handwriting examination: Meeting the challenges of science and the law. Forensic Sci Commun 11(4):1–13

    Google Scholar 

  • Hassaïne A, Al-Maadeed S, Jaam J, Jaoua A (2013) ICDAR 2013 competition on gender prediction from handwriting. In: 2013 12th international conference on document analysis and recognition, pp 1417–1421

  • Ibrahim AS, Youssef A, Abbott AL (2014) Global vs. local features for gender identification using Arabic and English handwriting. In: 2014 IEEE international symposium on signal processing and information technology (ISSPIT), pp 000155–000160

  • Ibrahim AS, Youssef AE, Abbott AL (2015) Global vs. local features for gender identification using Arabic and English handwriting. In: 2014 IEEE international symposium on signal processing and information technology, ISSPIT. vol 2014, pp 155–160. https://doi.org/10.1109/ISSPIT.2014.7300580

  • Illouz E, David E, Netanyahu NS (2018) Handwriting-based gender classification using end-to-end deep neural networks. In: arXiv (Vol 1). Springer International Publishing. https://doi.org/10.1007/978-3-030-01424-7

  • Kaur H, Kumar M (2018) A comprehensive survey on word recognition for non-Indic and Indic scripts. Pattern Anal Applic 21:897–929. https://doi.org/10.1007/s10044-018-0731-2

    Article  MathSciNet  Google Scholar 

  • Kaur H, Kumar M (2021) performance evaluation of various feature selection techniques for offline handwritten gurumukhi place name recognition. In: Singh TP, Tomar R, Choudhury T, Perumal T, Mahdi HF (eds) Data driven approach towards disruptive technologies studies in autonomic, data-driven and industrial computing. Springer, Singapore

    Google Scholar 

  • Kaur RP, Jindal MK, Kumar M (2021) Newspaper text recognition printed in Gurumukhi Script: SVM Versus MLP. In: Singh TP, Tomar Ravi, Choudhury Tanupriya, Perumal T, Mahdi HF (eds) Data driven approach towards disruptive technologies: proceedings of MIDAS 2020. Springer Singapore, Singapore, pp 23–37. https://doi.org/10.1007/978-981-15-9873-9_3

    Chapter  Google Scholar 

  • Kleber F, Fiel S, Diem M, Sablatnig R (2013) CVL-DataBase: an off-line database for writer retrieval, writer identification and word spotting. In: 2013 12th international conference on document analysis and recognition, pp 560–564

  • Kumar M, Jindal MK, Sharma RK, Jindal SR (2018a) A novel framework for writer identification based on pre-segmented Gurmukhi characters. Sadhana - Acad Proc Eng Sci 43(12):1–9. https://doi.org/10.1007/s12046-018-0966-z

    Article  Google Scholar 

  • Kumar M, Jindal S, Jindal MK, Lehal GS (2018) Improved recognition results of medieval handwritten Gurmukhi manuscripts using boosting and bagging methodologies. Neural Proc Lett 50:43–56

    Article  Google Scholar 

  • Kumar M, Jindal MK, Sharma R, Jindal S (2019) Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study. Artif Intell Rev 53:2075–2097

    Article  Google Scholar 

  • Kumar M, Gupta S, Mohan N (2020) A computational approach for printed document forensics using SURF and ORB features. Soft Comput. https://doi.org/10.1007/s00500-020-04733-x

    Article  Google Scholar 

  • Liwicki M, Schlapbach A, Bunke H (2011) Automatic gender detection using on-line and off-line information. Pattern Anal Appl 14(1):87–92. https://doi.org/10.1007/s10044-010-0178-6

    Article  MathSciNet  Google Scholar 

  • Liwicki M, Bunke H (2005) IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard. In: Proceedings of the 8th internetional conference on document analysis and recognition, vol 2, pp 956–961

  • Mahmoud SA, Ahmad I, M Alshayeb, Al-Khatib WG, Parvez MT, Fink GA, Margner V, Haikal El Abed (2012) KHATT: Arabic offline handwritten text database, 13th international conference on frontiers in handwriting recognition (ICFHR), pp 447–452

  • Maji P, Chakraborty S, Samanta S, Chatterjee S, Kausar N, Dey N (2015) Effect of euler number as a feature in gender recognition system from offline handwritten signature using neural networks. In: 2015 international conference on computing for sustainable global development, INDIA Com vol 2015, pp 1869–1873.

  • Maken P, Gupta A, Gupta M (2019) A study on various techniques involved in gender prediction system: a comprehensive review. Cyber Inform Technol 19(2):51–73. https://doi.org/10.2478/cait-2019-0015

    Article  Google Scholar 

  • Marti U, Bunke H (1999) A full English sentence database for off-line handwriting recognition. In: Proceedings of the fifth international conference on document analysis and recognition. ICDAR '99 (Cat. No.PR00318), pp 705–708

  • Marti U, Bunke H (2000) Handwritten sentence recognition. In Proc. of the 15th Int. Conf. on Pattern Recognition, Vol 3, pp 467–470

  • Marti U, Bunke H (2002) The IAM-database: an English sentence database for offline handwriting recognition. Int J Doc Anal Recogn 5:39–46

    Article  MATH  Google Scholar 

  • Mirza A, Moetesum M, Siddiqi I, Djeddi C (2016) Gender classification from offline handwriting images using textural features. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, pp 395–398. https://doi.org/10.1109/ICFHR.2016.0080

  • Moetesum M, Siddiqi I, Djeddi C, Hannad Y, Al-Maadeed S (2018) Data driven feature extraction for gender classification using multi-script handwritten texts. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, 2018-Augus, pp 564–569. https://doi.org/10.1109/ICFHR-2018.2018.00104

  • Morera Á, Sánchez Á, Vélez JF, Moreno AB (2018) Gender and handedness prediction from offline handwriting using convolutional neural networks. Complexity. https://doi.org/10.1155/2018/3891624

    Article  MathSciNet  Google Scholar 

  • Morris RN (2021) forensic handwriting identification: fundamental concepts and principles. Academic Press, Cambridge, pp 1–297

    Book  Google Scholar 

  • Narang SR, Jindal MK, Kumar M (2019) Devanagari ancient character recognition using DCT features with adaptive boosting and bootstrap aggregating. Soft Comput 23:13603–13614

    Article  Google Scholar 

  • Narang SR, Jindal M, Ahuja S, Kumar M (2020) On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features. Soft Comput 24:17279–17289

    Article  Google Scholar 

  • Navya BJ, Shivakumara P, Shwetha GC, Roy S, Guru DS, Pal U, Lu T (2018a) Adaptive multi-gradient kernels for handwriting based gender identification. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, 2018a-Augus, pp 392–397. https://doi.org/10.1109/ICFHR-2018.2018.00075

  • Navya BJ, Swetha GC, Shivakumara P, Roy S, Guru DS, Pal U, Lu T (2018b) Multi-gradient directional features for gender identification. In: Proceedings - international conference on pattern recognition, 2018b-Augus, pp 3657–3662. https://doi.org/10.1109/ICPR.2018.8546033

  • Pandey P, Seeja KR (2018) Forensic writer identification with projection profile representation of graphemes. In: Somani AK, Srivastava S, Mundra A, Rawat S (eds) Proceedings of first international conference on smart system, innovations and computing. Springer Singapore, Singapore, pp 129–136. https://doi.org/10.1007/978-981-10-5828-8_13

    Chapter  Google Scholar 

  • Pechwitz M, El Abed H, Märgner V (2012) Handwritten Arabic word recognition using the IFN/ENIT-database. In: Märgner V, El Abed H (eds) Guide to OCR for Arabic Scripts. Springer, London

    Google Scholar 

  • Pietikainen M, Rosenfeld A (1982) Gray level pyramid linking as an aid in texture analysis. In: IEEE TranSo Systems, Man, Cybernetics SMC-12, pp 422–429

  • Purohit N, Panwar S (2021) State-of-the-Art: offline writer identification methodologies. In: 2021 international conference on computer communication and informatics (ICCCI), pp 1–8, https://doi.org/10.1109/ICCCI50826.2021.9402539

  • Rahmanian M, Shayegan MA (2021) Handwriting-based gender and handedness classification using convolutional neural networks. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-10170-7

    Article  Google Scholar 

  • Rehman A, Naz S, Razzak MI (2019) Writer identification using machine learning approaches: a comprehensive review. Multimed Tools Appl 78(8):10889–10931. https://doi.org/10.1007/s11042-018-6577-1

    Article  Google Scholar 

  • Riza LS, Zainafif A, Rasim S, Nazir S (2018) Fuzzy rule-based classification systems for the gender prediction from handwriting. Telkomnika Telecommun Comput Electron Control 16(6):2725–2732

    Google Scholar 

  • Said H, Tan T, Baker K (2000) Personal identification based on handwriting. Pattern Recognit 33:149–160

    Article  Google Scholar 

  • Shahabi F, Rahmati M (2009) A new method for writer identification of handwritten farsi documents. In: 2009 10th international conference on document analysis and recognition, pp 426–430

  • Siddiqi I, Djeddi C, Raza A, Souici-meslati L (2015) Automatic analysis of handwriting for gender classification. Pattern Anal Appl 18(4):887–899. https://doi.org/10.1007/s10044-014-0371-0

    Article  MathSciNet  Google Scholar 

  • Siddiqi I, Vincent N (2009) A set of chain code based features for writer recognition. In: 2009 10th international conference on document analysis and recognition, pp 981–985

  • Sokic E, Salihbegovic A, Ahic-Djokic M (2012) Analysis of off-line handwritten textsamples of different gender using shape descriptors. In: 2012 9th international symposium on telecommunications, BIHTEL 2012 Proceedings. https://doi.org/10.1109/BIHTEL.2012.6412086

  • Srihari SN, Cha SH, Arora H, Lee S (2002) Individuality of handwriting. J Forensic Sci 47(4):856–872

    Article  Google Scholar 

  • Tan J, Lai J-H, Wang P, Bi N (2015) Multiscale region projection method to discriminate between printed and handwritten text on registration forms. Int J Pattern Recognit Artif Intell 29(8):153–185

    Article  MathSciNet  Google Scholar 

  • Tan J, Bi N, Suen CY, Nobile N (2016) Multi-feature selection of handwriting for gender identification using mutual information. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, pp 578–583. https://doi.org/10.1109/ICFHR.2016.0111

  • Topaloglu M, Ekmekci S (2017) Gender detection and identifying one’s handwriting with handwriting analysis. Expert Syst Appl 79:236–243. https://doi.org/10.1016/j.eswa.2017.03.001

    Article  Google Scholar 

  • Venugopal V, Sundaram S (2017) An online writer identification system using regression-based feature normalization and codebook descriptors. Expert Syst Appl 72:196–206

    Article  Google Scholar 

  • Viard-Gaudin C, Lallican P, Binter P, Kn-err S (1999) The IRESTE On/Off (IRONOFF) dual handwriting database. In: Proceedings of the fifth international conference on document analysis and recognition, ICDAR IEEE Computer Society.747Washington, DC, USA p 455

  • Wang T, Wu DJ, Coates A, Ng A (2012) End-to-end text recognition with convolutional neural networks. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp 3304–3308

  • Wu Y, Lu H, Zhang Z (2017) Text-independent online writer identification using hidden markov models. In: IEICE Trans. Inf. Syst., 100-D, pp 332–339

  • Xing L, Qiao Y (2016) DeepWriter: a multi-stream deep CNN for text-independent writer identification. In: 2016 15th international conference on frontiers in handwriting recognition (ICFHR), 584–589

  • Xue G, Liu S, Gong D, Ma Y (2020) ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05237-3

    Article  Google Scholar 

  • Yang W, Jin L, Liu M (2016) DeepWriterID: an end-to-end online text-independent writer identification system. IEEE Intell Syst 31:45–53

    Article  Google Scholar 

  • Youssef AE, Ibrahim AS, Lynn Abbott A (2013) Automated gender identification for Arabic and English handwriting. In: 5th international conference on imaging for crime detection and prevention. https://doi.org/10.1049/ic.2013.0274

  • Zois EN, Anastassopoulos V (2000) Morphological waveform coding for writer identification. Pattern Recognit 33:385–398

    Article  Google Scholar 

  • Zou W, Xia Y, Cao W (2022a) Back-propagation extreme learning machine. Soft Comput 26(18):9179–9188

    Article  Google Scholar 

  • Zou W, Xia Y, Cao W (2022b) Broad learning system based on driving amount and optimization solution. Eng Appl Artif Intell 116:105353

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Application, Panjab University, Chandigarh, India

    Monika Sethi

  2. P.G. Department of Information Technology, Goswami Ganesh Dutta Sanatan Dharma College, Chandigarh, India

    Monika Sethi

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  4. Department of Computer Science and Applications, Panjab University Regional Centre, Muktsar, India

    M. K. Jindal

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  1. Monika Sethi
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Sethi, M., Kumar, M. & Jindal, M.K. Gender prediction system through behavioral biometric handwriting: a comprehensive review. Soft Comput 27, 6307–6327 (2023). https://doi.org/10.1007/s00500-023-07907-5

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