Biometric recognition using online uppercase handwritten text

Sesa-Nogueras, Enric; Faundez-Zanuy, Marcos

doi:10.1016/j.patcog.2011.06.002

Cited by 47 publications

(37 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may well be that pen-up strokes contain significantly less gender-related information than pen-down strokes, although in the field of writer identification and writer verification they can perform almost equally well [46,17,55]. The source of the difference could also lie with the classification method itself, although the very same method achieves notably good results with pen-down strokes.…”

Section: Discussionmentioning

confidence: 99%

“…In our past research [46,17], it has been shown that although a single online word may contain enough information to identify its author with notable accuracy (close to the accuracy achieved with signatures), the combination of Tables 8, 9 and 10 summarize the results obtained when the whole set of sixteen words in the BiosecurID database is considered and their sixteen individual measures (distortions) are combined into a single one. Again, the combination strategy is the straightforward non-weighted addition of the individual measures.…”

Section: Classification Rates Combining the Sixteen Wordsmentioning

confidence: 99%

See 1 more Smart Citation

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

2015

Self Cite

View full text Add to dashboard Cite

This paper presents a gender-classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a male or a female. The method proposed is allographic, regarding strokes as the structural units of handwriting. Strokes performed while the writing device is not exerting any pressure on the writing surface, pen-up (in-air) strokes, are also taken into account. The method is also text-dependent meaning that training and testing is done with exactly the same text. Text-dependency allows classification be performed with very small amounts of text. Experimentation, performed with samples from the BiosecurID database, yields results that fall in the range of the classification averages expected from human judges. With 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 aforementioned rates are highly significant. In order to explore the classification potential of the pen-up strokes, these are also considered. Although in this case, results are not conclusive, and an outstanding average of 74 % of well-classified writers is obtained when information from pen-up strokes is combined with information from pen-down ones.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Classification Rates Combining the Sixteen Wordsmentioning

confidence: 99%

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

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The pen-up in-formation is represented in Fig 1 using "+" while the pen-down is marked with "*". Our experiments on the biometric recognition of people reveal that these two kinds of information are complementary [1] and in fact, contain a similar discriminative capability, even when using a database of 370 users [2][3]. In the medical field, the study of handwriting has proven to be an aid to diagnose and track some diseases of the nervous system.…”

Section: Introductionmentioning

confidence: 83%

Online Drawings for Dementia Diagnose: In-Air and Pressure Information Analysis

et al. 2014

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper we present experimental results comparing on-line drawings for control population (left and right hand) as well as Alzheimer disease patients. The drawings have been acquired by means of a digitizing tablet, which acquires time information angles and pressures. Experimental measures based on pressure and in-air movements appear to be significantly different for both groups, even when control population performs the tasks with the non-dominant hand.

show abstract

“…The digitizing tablet acquires 200 samples per second, including the spatial coordinates (x,y), the pressure, altitude and azimuth [14]. The digitizer provides accurate measurement when the pen is touching the tablet and when it is lifted 6 mm above the digitizer [15].…”

Section: Methodsmentioning

confidence: 99%