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Improving fingerprint based gender identification technique using systematic pixel counting

https://doi.org/10.1109/iceeict.2015.7307487
0 signed · 11 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/s0379-0738(99)00037-7openalex-snapshot
Is there a gender difference in fingerprint ridge density?
inferredcites →https://doi.org/10.4314/ijmu.v3i2.39838openalex-snapshot
Gender Determination using Fingertip Features
inferredcites →https://doi.org/10.5005/jp/books/11366openalex-snapshot
Textbook of Forensic Medicine and Toxicology
inferredcites →https://doi.org/10.5455/2320-6012.ijrms20140207openalex-snapshot
Dermatoglyphics: in health and disease - a review
inferredcites →https://doi.org/10.56726/irjmets49874openalex-snapshot
Fingerprint Based Gender Classification
inferredcites →https://doi.org/10.9790/3021-03850509openalex-snapshot
Age and Gender Determination from Finger Prints using RVA and dct Coefficients

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3444032openalex-extracted
Gender Detection and Classification from Fingerprints Using Pixel Count
inferred← citeshttps://doi.org/10.1007/978-3-030-31456-9_5openalex-snapshot
3D Fingerprint Gender Classification Using Deep Learning
inferred← citeshttps://doi.org/10.1080/19393555.2020.1741742openalex-snapshot
Gender classification from fused multi-fingerprint types
inferred← citeshttps://doi.org/10.1109/icspc57692.2023.10125703openalex-snapshot
Gender Detection and Classification from Fingerprints Using Convolutional Neural Network
inferred← citeshttps://doi.org/10.12928/telkomnika.v15i1.6113openalex-snapshot
The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Stepping Exercise
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