Age detection from handwriting using different feature classification models

AL-Qawasmeh, Najla; Khayyat, Muna; Suen, Ching Y.

doi:10.1016/j.patrec.2023.02.001

Cited by 3 publications

(3 citation statements)

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“…Mirza et al studied the influence of handwriting image's visual appearance on the author's gender and used Gabor filters to extract texture information for gender classification [26]. Najla et al addressed the age detection problem by dividing age into two groups, young adult writers and mature adult writers [1]. They extracted main features including Irregularity in pen pressure (IPP), irregularity in slant (IS), irregularity in text line (TLI), and the percentage of white and black pixels (PWB), and used SVM and neural network (NN) for classification.…”

Section: Related Workmentioning

confidence: 99%

“…( 1). Given the input X , the horizontal and vertical coordinates are encoded on each channel by using pooling kernels with sizes of ( 1) H or ( 1) W , respectively. The output of the c -th channel with height h , denoted by ()…”

Section: Proposed Model 1) Resnet Modelmentioning

confidence: 99%

“…Handwritten images can be used not only for text and signature recognition but also for biometric identification based on demographic features such as age range, gender, handedness, ethnicity, etc. [1][2][3][4]. The correlation between these attributes and handwriting has been proposed in study [5], with various applications such as customer identity verification in banks, government institutions, and other financial organizations as well as limiting the investigation scope to a limited group of individuals in forensic science.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

Zhao,

Wu,

Chen

2024

ijacsa

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Currently, age estimation is a hot research topic in the field of forensic biology. Age estimation methods based on facial or brain features are easily affected by external factors. In contrast, handwriting analysis is a more reliable method for age estimation. This paper aims to improve the accuracy and efficiency of age prediction using handwriting analysis by proposing a novel method that integrates a coordinate attention mechanism in a deep residual network (CA-ResNet). This method can more accurately capture important features in the input handwritten images while reducing the number of model parameters, thereby improving the accuracy (Acc) and efficiency of the model for age estimation. The proposed method is evaluated on standard handwriting datasets and the created dataset, and it is compared with the current state-of-the-art methods. The results show that the method consistently outperforms others, achieving an accuracy of 79.60% on the IAM handwriting dataset, with a 6.31% improvement over other methods.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Model 1) Resnet Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

Zhao,

Wu,

Chen

2024

ijacsa

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Review of age and gender detection methods based on handwriting analysis

Alaei,

Alaei

2023

Neural Comput & Applic

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Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation.

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