Ahmed, M., Rasool, A. G., Afzal, H., & Siddiqi, I. (2017). Improving handwriting-based gender classification using ensemble classifiers. Expert Systems with Applications, 85, 158-168.
Al Maadeed, S., & Hassaine, A. (2014). Automatic prediction of age, gender, and nationality in offline handwriting. EURASIP Journal on Image and Video Processing, 2014(1), 10.
Amend, K. K., & Ruiz, M. S. (2000). Handwriting analysis: The complete basic book. Red Wheel/Weiser.
Bangerter, A., König, C. J., Blatti, S., & Salvisberg, A. (2009). How widespread is graphology in personnel selection practice? A case study of a job market myth. International Journal of Selection and Assessment, 17(2), 219-230.
Bhunia, A. K., Das, A., Bhunia, A. K., Kishore, P. S. R., & Roy, P. P. (2019). Handwriting recognition in low-resource scripts using adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4767-4776).
Blumenstein, M., Verma, B., & Basli, H. (2003, August). A novel feature extraction technique for the recognition of segmented handwritten characters. In Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings. (pp. 137-141). IEEE.
Champa, H. N., & AnandaKumar, K. R. (2010, August). Automated human behavior prediction through handwriting analysis. In 2010 First International Conference on Integrated Intelligent Computing (pp. 160-165). IEEE.
Chaudhari, K., & Thakkar, A. (2019). Survey on handwriting-based personality trait identification. Expert Systems with Applications, 124, 282-308.
Cobb-Clark, D. A., & Schurer, S. (2012). The stability of big-five personality traits. Economics Letters, 115(1), 11-15.
Coll, R., Fornés, A., & Lladós, J. (2009, July). Graphological analysis of handwritten text documents for human resources recruitment. In 2009 10th International Conference on Document Analysis and Recognition (pp. 1081-1085). IEEE.
Dahlen, E. R., & White, R. P. (2006). The Big Five factors, sensation seeking, and driving anger in the prediction of unsafe driving. Personality and individual differences, 41(5), 903-915.
Gavrilescu, M., & Vizireanu, N. (2018). Predicting the big five personality traits from handwriting. EURASIP Journal on Image and Video Processing, 2018(1), 57.
Giluk, T. L. (2009). Mindfulness, Big Five personality, and affect: A meta-analysis. Personality and Individual Differences, 47(8), 805-811.
He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., & Li, M. (2019). Bag of tricks for image classification with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 558-567).
James, K. H., & Engelhardt, L. (2012). The effects of handwriting experience on functional brain development in pre-literate children. Trends in neuroscience and education, 1(1), 32-42.
Joshi, P., Ghaskadbi, P., & Tendulkar, S. (2018, July). A Machine Learning Approach to Employability Evaluation Using Handwriting Analysis. In International Conference on Advanced Informatics for Computing Research (pp. 253-263). Springer, Singapore.
Kim, J., Kim, T., Kim, S., & Yoo, C. D. (2019). Edge-labeling graph neural network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 11-20).
Klimoski, R. J., & Rafaeli, A. (1983). Inferring personal qualities through handwriting analysis. Journal of Occupational Psychology, 56(3), 191-202.
Komarraju, M., Karau, S. J., Schmeck, R. R., & Avdic, A. (2011). The Big Five personality traits, learning styles, and academic achievement. Personality and individual differences, 51(4), 472-477.
Landers, R. N., & Lounsbury, J. W. (2006). An investigation of Big Five and narrow personality traits in relation to Internet usage. Computers in human behavior, 22(2), 283-293.
Lee, G. C., Yeh, F. H., Chen, Y. J., & Chang, T. K. (2017). Robust handwriting extraction and lecture video summarization. Multimedia Tools and Applications, 76(5), 7067-7085.
Li, H., Eigen, D., Dodge, S., Zeiler, M., & Wang, X. (2019). Finding task-relevant features for few-shot learning by category traversal. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-10).
Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Huang, J., ... & Feng, L. (2014, November). User-level psychological stress detection from social media using deep neural network. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 507-516).
Luria, G., & Rosenblum, S. (2012). A computerized multidimensional measurement of mental workload via handwriting analysis. Behavior research methods, 44(2), 575-586.
Luria, G., Kahana, A., & Rosenblum, S. (2014). Detection of deception via handwriting behaviors using a computerized tool: Toward an evaluation of malingering. Cognitive Computation, 6(4), 849-855.
Mouly, S., Mahé, I., Champion, K., Bertin, C., Popper, P., De Noblet, D., & Bergmann, J. F. (2007). Graphology for the diagnosis of suicide attempts: a blind proof of principle controlled study. International journal of clinical practice, 61(3), 411-415.
Poznanski, A., & Wolf, L. (2016). Cnn-n-gram for handwriting word recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2305-2314)
Siddiqi, I., Djeddi, C., Raza, A., & Souici-Meslati, L. (2015). Automatic analysis of handwriting for gender classification. Pattern Analysis and Applications, 18(4), 887-899.
Sueiras, J., Ruiz, V., Sanchez, A., & Velez, J. F. (2018). Offline continuous handwriting recognition using sequence to sequence neural networks. Neurocomputing, 289, 119-128.
Sun, Q., Liu, Y., Chua, T. S., & Schiele, B. (2019). Meta-transfer learning for few-shot learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 403-412).
Tang, T. L. P. (2012). Detecting honest people’s lies in handwriting. Journal of Business Ethics, 106(4), 389-400.
Wertheimer, D., & Hariharan, B. (2019). Few-shot learning with localization in realistic settings. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6558-6567).
Wertheimer, D., & Hariharan, B. (2019). Few-shot learning with localization in realistic settings. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6558-6567).
Yang, K., Mall, S., & Glaser, N. (2017). Prediction of personality first impressions with deep bimodal LSTM. Technical report, arXiv, 2017. URL http://cs231n. stanford. edu/reports/2017/pdfs/713. pdf.