Abstract
In recent years, fingerprint analysis has expanded to include the estimation of age information, providing an additional layer of personal identification because of its uniqueness and permanence. In this study, 2D-Discrete Wavelet Transform (DWT) and 2D-Discrete Fourier transform (DFT) are used for fingerprint-based age group classification and their performance is compared against fingerprint dataset which is distributed among various age groups from the age of 10 to 90 including both genders. The face-based age group classification techniques have limited practice for crime scene investigation due to their dependency on the availability of vast global features, that is, complete face image. The research gap identifies that the accuracy between 2D-DWT and 2D-DFT, varied, and this is proved even for the unbalanced age group classes collected in this study’s real-time database and its corresponding results. The three variants of 2D-DWT Level-4, level-3, level-2 and 2D-DFT are considered for this research work and the most accurate was the DWT Level-4 which classified the age group using the fingerprint images with an astonishing accuracy 99.96 which is verified and supported by detailed analysis of all classification metrics.
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Sanjay Gaikwad, A., Musande, V.B. (2023). A Novel Fingerprint-Based Age Group Classification Approach Using DWT and DFT Analysis. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_26
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DOI: https://doi.org/10.1007/978-981-99-7093-3_26
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