Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach

Sesa-Nogueras, Enric; Faundez-Zanuy, Marcos; Roure-Alcobe, Josep

doi:10.1007/s12559-015-9332-1

Cited by 25 publications

(11 citation statements)

References 46 publications

(69 reference statements)

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“…For text-based recognition the allographic text-dependent recognition system (ATDR) presented in [22] has been applied. This approach yields performances close to those obtained with signature and, with minor variations, it has also been successfully applied to gender recognition [23]. It fully exploits online data, and benefits from the information provided by in-air trajectories, and from the combination of in-air and onsurface signals.…”

Section: Text-based Recognitionmentioning

confidence: 72%

The Effect of Fatigue on the Performance of Online Writer Recognition

Sesa-Nogueras¹,

Faundez-Zanuy²,

Garnacho-Castaño³

2021

Cogn Comput

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Background: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject's state. Fatigue is one of the conditions that can significantly affect the outcome of handwriting tasks. Recent research has already shown that physical fatigue produces measurable differences in some features extracted from common writing and drawing tasks. It is important to establish to which extent physical fatigue contributes to the intra-person variability observed in these biometric modalities and also to know whether the performance of recognition methods is affected by fatigue. Goal: In this paper we assess the impact of fatigue on intra-user variability and on the performance of signature-based and text-based writer recognition approaches encompassing both identification and verification. Methods: Several signature and text recognition methods are considered and applied to samples gathered after different levels of induced fatigue, measured by metabolic and mechanical assessment and, also by subjective perception. The recognition methods are Dynamic Time Warping and Multi Section Vector Quantization, for signatures, and Allographic Text-Dependent Recognition for text in capital letters. For each fatigue level, the identification and verification performance of these methods is measured. Results: Signature shows no statistically significant intra-user impact, but text does. On the other hand, performance of signature-based recognition approaches is negatively impacted by fatigue whereas the impact is not noticeable in text-based recognition, provided long enough sequences are considered.

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Section: Text-based Recognitionmentioning

confidence: 72%

The Effect of Fatigue on the Performance of Online Writer Recognition

Sesa-Nogueras¹,

Faundez-Zanuy²,

Garnacho-Castaño³

2021

Cogn Comput

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“…As examples of gender recognition based on handwriting, we can mention the following works. In [31] using only four repetitions of a single uppercase word, the average rate of well-classified writers is 68 %; with sixteen words, the rate rises to an average of 72.6 %. Statistical analysis reveals that the rates mentioned above are highly significant.…”

Section: Metadata Applicationsmentioning

confidence: 98%

“…The challenges and opportunities of handwriting biometrics for e-security and e-health outlined before are particularly well suited to be advanced in the framework of Cognitive Computing. Combined work considering at the same time both disciplines (Biometrics and Cognitive Computing) can be seen in a few selected works in the past [31][61][74] [6], but still many synergies between them are to be exploited in future research and development.…”

Section: Introductionmentioning

confidence: 99%

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

et al. 2020

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Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and applications based on handwriting signals. Later on, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide for future research. Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the risks inherent when using these behavioral signals.

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“…It was studied by Graphonomics and Psychology in a nonautomatic form since the beginning of last century [24,25]. One of the first automatic methods to classify gender from offline handwriting was presented by Hecker in 1996 [26].…”

Section: Related Workmentioning

confidence: 99%

Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

et al. 2018

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Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones. Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addressed before by other researchers. Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems. Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.

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Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach

Cited by 25 publications

References 46 publications

The Effect of Fatigue on the Performance of Online Writer Recognition

The Effect of Fatigue on the Performance of Online Writer Recognition

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

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