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PCA-based gender classification system using hybridization of features and classification techniques

Abstract

Gender classification system is an automated, challenging, and efficacious system due to the analogous vision of males and females with handwriting. Gender classification is a binary problem that is based on physiological and behavioral biometric traits. It is widely used for forensic-based investigations, autopsy determination, suspected areas, and questioned documents. The proposed study has been considered for the behavioral biometric trait, i.e., handwriting in the Gurumukhi script. The novelty of this study can be seen from the three perspectives, i.e., hybridization of feature extraction techniques, principal component analysis (PCA) for dimensionality reduction, and hybridization of classification techniques. Zoning, Diagonal, Transition, and Peak Extent-based feature extractions were implemented followed by their hybridization. The classification techniques such as Decision Trees, Random Forest, and Extreme Gradient Boosting classifiers were experienced with the hybridized approach using a majority voting scheme. Also, the comparison and critical analysis of gender classification accuracy with CPU elapsed time has also been presented before and after implementing PCA. There are many novel ideas for the researchers such as gender classification through online handwriting, prediction of age, personality, state, nationality, and stress prediction through handwriting, and further gender classification problem can be enhanced to third or trans-gender too using handwriting as a biometric modality. The system can be implemented with many other Indic scripts and presenting novel track in the handwriting-based researches.

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References

  1. Agrawal B, Dixit M (2020). Age Estimation and Gender Prediction Using Convolutional Neural Network. In: Pandit M., Srivastava L., Venkata Rao R., Bansal J. (eds) Intelligent Computing Applications for Sustainable Real-World Systems. ICSISCET 2019. In Proceedings in Adaptation, Learning and Optimization, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-44758-8_15

  2. Aggarwal A, Singh K (2015). Handwritten Gurumukhi character recognition. In Proceedings of the Computer Communication and Control (IC4). 1–5.

  3. Ahmed M, Rasool AG, Afzal H, Siddiqi I (2017) Improving handwriting-based gender classification using ensemble classifiers. Expert System with Applications 85:158–168

    Article  Google Scholar 

  4. Akbari M, Nouri K, Sadri J, Djeddi C, Siddiqi I (2017) Wavelet based Gender Detection on Offline handwritten documents using Probabilistic Finite State Automata. Image vis Comput 59:17–30

    Article  Google Scholar 

  5. Aubin V, Mora M (2017) A New Descriptor for Person Identity Verification based on Handwritten Strokes Off-Line Analysis. Expert System with Applications 89:241–253

    Article  Google Scholar 

  6. Bartle A, Zheng J (2015). Gender classification with Deep Learning. Stanfordcs, 224d Course Project Report, 1–7.

  7. 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 

  8. Botchkarev A (2019). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. Interdisciplinary Journal of Information, Knowledge, and Management, 14:45–79, arXiv preprint arXiv:1809.03006.

  9. Bouadjenek N, Nemmour H, Chibani Y (2015) Histogram of Oriented Gradients for writer’s gender, handedness and age prediction. Proceedings of International Conference on Innovations in Intelligent Systems and Applications (INISTA). https://doi.org/10.1109/INISTA.2015.7276752

    Article  Google Scholar 

  10. Bouadjenek N, Nemmour H, Chibani Y (2015b). Age, gender and handedness prediction from handwriting using gradient features. In Proceedings of the 13th International Conference on Document Analysis and Recognition, 1116–1120.

  11. Cordasco G, Buonanno M, Faundez-Zanuy M, Riviello MT, Sulem LL, Esposito A (2020). Gender Identification through Handwriting: An Online Approach. In Proceedings of. 11th IEEE International Conference on Cognitive Info communications (CogInfoCom), 000197–000202, Mariehamn, Finland, DOI: https://doi.org/10.1109/CogInfoCom50765.2020.9237863.

  12. Dargan S, Kumar M (2019) Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-018-9278-z

    Article  Google Scholar 

  13. Dargan S, Kumar M (2020). A Comprehensive survey on the biometric recognition systems based on physiological and behavioural modalities. Expert Systems with Applications, 143:113114.

  14. Faundez-Zanuy M, Fierrez J, Ferrer MA (2020) Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health. Cognitive Computing 12:940–953

    Article  Google Scholar 

  15. Gattal A, Djeddi C, Siddiqi I, Chibani Y (2018) Gender Classification from Online multi-script handwriting images using Oriented Basic Image Features (OBIF). Expert System with Applications 99:155–167

    Article  Google Scholar 

  16. Gattal A, Djeddi C, Bensefia A, EnnajiA (2020). In: El Moataz A., Mammass D., Mansouri A., Nouboud F. (eds) Image and Signal Processing. ICISP 2020. Lecture Notes in Computer Science, vol 12119. Springer, Cham. https://doi.org/10.1007/978-3-030-51935-3_25Handwriting Based Gender Classification Using COLD and Hinge Features, 233–242.

  17. Illouz E, David E, Netanyahu NS (2018). Handwriting- Based Gender Classification Using End-to-End Deep Neural Networks. In Proceedings of International Conference on Artificial Neural Networks, 613–621.

  18. Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: Accomplishments, challenges, and opportunities. Pattern Recogn Lett 79:80–105

    Article  Google Scholar 

  19. James J, Lakshmi C, Kiran U, Parthiban A (2019) An efficient offline handwritten character recognition using CNN and XGBoost. Int J Innov Technol Explor Eng (IJITEE) 8(6):115–118

    Google Scholar 

  20. Kaur H, Kumar M (2021) Offline handwritten Gurumukhi word recognition using eXtreme Gradient Boosting methodology. Soft Comput 25:4451–4464. https://doi.org/10.1007/s00500-020-05455-w

    Article  Google Scholar 

  21. Kumar M, Sharma RK, Jindal MK (2013a) A novel feature extraction technique for offline handwritten Gurumukhi character recognition. IETE J Res 59:687–691

    Article  Google Scholar 

  22. Kumar M, Sharma RK, Jindal MK (2013b) PCA Based Offline Handwritten Gurmukhi Character Recognition. Smart Computing Review 3(5):346–357

    Article  Google Scholar 

  23. Kumar M, Sharma RK, Jindal MK (2014) Efficient Feature Extraction Techniques for Offline Handwritten Gurumukhi Character Recognition. National Academy Science Letters 37:381–391

    Article  Google Scholar 

  24. Kumar M, Jindal MK, Sharma RK, Jindal SR (2020) Performance evaluation of classifiers for the recognition of offline handwritten Gurumukhi characters and numerals: A study. Artif Intell Rev 53:2075–2097

    Article  Google Scholar 

  25. Kumar S, Singh S, Kumar J (2019). Gender Classification Using Machine Learning with Multi-Feature Method. In Proceedings of IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, pp 0648–0653. https://doi.org/10.1109/CCWC.2019.8666601.

  26. Lee J, Lin C, Huang C (2013) Novel features selection for gender classification. In: Proceedings of International Conference on Mechatronics and Automation, Takamatsu, pp 785–790

  27. Liwicki M, Schlapbach A, Bunke H (2011) Automatic Gender Detection using Online and Offline Information. Pattern Anal Appl 14(1):87–92

    MathSciNet  Article  Google Scholar 

  28. Maadeed AI, Hassaine A (2014) Automatic prediction of Age, Gender, and Nationality in offline handwriting. EURASIP J Image Video Process 1(10):1–10. https://doi.org/10.1186/1687-5281-2014-10

  29. Maken P, Gupta A (2021) A method for automatic classification of gender based on text- independent handwriting. Multimed Tools Appl 80:24573–24602

    Article  Google Scholar 

  30. Mirza A, Moetesum M, Siddiqi I, Djeddi C (2016). Gender classification from Offline handwriting images using Textural Features. In Proceedings of the 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 395–398.

  31. Morera A, Sanchez A, Velez JF, Moreno A (2018). Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks. Complexity, 1–14.

  32. Nader L, Mohamed A, Nazir M, Awadalla M (2018) Identification of Writer's Gender using Handwriting Analysis. Int J Sci Res Publ 8(10):1–28

    Google Scholar 

  33. Nguyen K, Fookes C, Sridharan S, Tistarelli M, Nixon M (2018) Super-Resolution for Biometrics: A Comprehensive Survey. Pattern Recogn 78:23–42

    Article  Google Scholar 

  34. Park S, Woo J (2019) Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum. Appl Sci 9(1249):1–12. https://doi.org/10.3390/app9061249

    MathSciNet  Article  Google Scholar 

  35. 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

  36. Sherwani F, Ibrahim BSKK, Asad MM (2020) Hybridized classification algorithms for data classification applications: A review. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2020.07.004

    Article  Google Scholar 

  37. Siddiqi I, Djeddi C, Raza A, Souici-Meslati, (2015) Automatic analysis of handwriting for Gender Classification. Pattern Anal Appl 18(4):887–899

    MathSciNet  Article  Google Scholar 

  38. Smirg O, Mikulka J, Faundez-Zanuy M, Grassi M, Mekyska J (2011) Gender Recognition Using PCA and DCT of Face Images. Advances in Computational Intelligence. https://doi.org/10.1007/978-3-642-21498-1_28

    Article  Google Scholar 

  39. Suri P K, Walia E, Verma E A (2011). Face detection and gender detection using principal component analysis (PCA). In Proceedings of IEEE 3rd International Conference on Communication Software and Networks, 679–684.doi: https://doi.org/10.1109/ICCSN.2011.6014983

  40. Swaminathan A, Chaba M, Sharma DK, Chaba Y (2020) Gender classification using facial embeddings: a novel approach. Proc Comput Sci 167:2634–2642. https://doi.org/10.1016/j.procs.2020.03.342

    Article  Google Scholar 

  41. Wong TT, Yang NY, Chen GH (2020) Hybrid classification algorithms based on instance filtering. Inf Sci 520:445–455. https://doi.org/10.1016/j.ins.2020.02.021

    Article  Google Scholar 

  42. Yiu T (2019) Understanding Random Forest, How the Algorithm Works and Why it Is So Effective. Towards Data Sci. https://towardsdatascience.com/understandingrandom-forest-58381e0602d2

  43. Youssef AE, Ibrahim AS, Abbott AL (2013). Automated Gender identification for Arabic and English handwriting. In Proceedings of 5th International Conference on Imaging for Crime Detection and Prevention, 1–6.

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Correspondence to Munish Kumar.

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Dargan, S., Kumar, M. & Tuteja, S. PCA-based gender classification system using hybridization of features and classification techniques. Soft Comput 25, 15281–15295 (2021). https://doi.org/10.1007/s00500-021-06118-0

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Keywords

  • Handwriting recognition
  • Biometric
  • Feature extraction
  • Dimensionality reduction
  • Hybridization techniques