A Hybrid Model by Combining Discrete Cosine Transform and Deep Learning for Children Fingerprint Identification

Kamble, Vaishali; Dale, Manisha; Bairagi, Vinayak K.

doi:10.14569/ijacsa.2023.0140186

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…al. [51] This showed accurate recognition of infants registered at 2 to 3 months and authenticated 3 months later with the accuracy of TAR=95.2% for the specified FAR=1.0Kamble et al [58], [59], [60], [61] combined the transform domain, Curve Discrete Cosine Transform features and machine learning classifier to achieve the accuracy of 96 % for identification of children. The transform domain deep learning approach is also applied to check the accuracy of children identification.…”

Section: Feature Extraction and Classification Algorithmsmentioning

confidence: 96%

Evolution in Children Fingerprint Recognition Approaches: A Review

H. Kamble,

P. Dale,

B. Chopade

et al. 2024

IJCDS

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Biometric as an identification tool for children recognition is started in the late 19th century by Sir Galton. However, it is still in the developing stage even after the span of two centuries. The main hurdles in this process are the small size, non-uniform growth of different biometrics and lack of public databases of children biometrics. The authors have touched all the aspects of fingerprint recognition of children. Childhood is very important and crucial in the life of human beings. Most important vaccinations are given in these years. Children are not able to take care of themselves therefore swapping, abduction and missing happens in this age. The main objective is to reveal the progression study of children's recognition for the age group of 0 to 5 years. The combination of transform domain features and machine learning classifiers gives good accuracy of identification of children. Also, multimodal fusion and deep learning approach will increase the identification accuracy of the children.In this paper, a complete survey of studies done for children recognition using physiological biometric is covered. Detailed discussion on database availability, scanning devices, feature extraction techniques, growth models used and matching algorithms is presented. Fingerprint modality is explored using their trends and challenges. Also, the effectiveness of fingerprint modality for recognition of children is discussed.

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Section: Feature Extraction and Classification Algorithmsmentioning

confidence: 96%

Evolution in Children Fingerprint Recognition Approaches: A Review

H. Kamble,

P. Dale,

B. Chopade

et al. 2024

IJCDS

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“…Shabil et al [25] used Pre-trained model of Keras applications with CNN custom by transfer learning for recognition of newborns and toddlers by using multiclass. Kamble V et al [26]…”

Section: Related Workmentioning

confidence: 99%

Developing an algorithm for Fingerprint Recognition of Newborns and Toddlers based on Deep Neural Networks and Transfer Learning

Mohammed Shabil, Dr. H.S. Fadewar

2023

tjjpt

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In the late 19th century, Sir Galton started the use of fingerprint recognition as a method for baby identification. However, even after the extent two centuries, fingerprint identification for newborns and toddlers has not advanced to the same level as that for adults. According to research from the International Centre for Missing and Exploited Children, there is a growing request for fingerprint identification of babies because more than a million of them go missing each year. So, we want to ensure that newborns and toddlers have access to their rights, including vaccinations, healthcare, and nutritional supplements until they reach school age. This paper develops an algorithm that has been used in previous research to recognize newborns and toddlers to produce remarkable and promising results by Pre-trained model deep neural networks and transfer learning to extract the deep features and to get the identification score, aiming to enhance accuracy and validate. The proposed model is validated on the fingerprint dataset of babies named as NITG dataset. The fingerprint recognition model proposed in the research showcases excellent results, where achieved 100% accuracy in training and 90.35% in validation.

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“…Deep learning is a kind of artificial intelligence that use neural networks to learn and extract characteristics from data automatically. Deep learning may be applied in fingerprint identification to reliably identify children's fingerprints by automatically learning and detecting the unique patterns and traits contained in their fingerprints [8][9][10]. One such application is in the area of children's safety.…”

Section: Introductionmentioning

confidence: 99%

Enhancing child safety with accurate fingerprint identification using deep learning technology

Sahy¹,

Niu²,

Khalaf³

et al. 2023

PEN

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Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children.

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A Hybrid Model by Combining Discrete Cosine Transform and Deep Learning for Children Fingerprint Identification

Cited by 6 publications

References 19 publications

Evolution in Children Fingerprint Recognition Approaches: A Review

Evolution in Children Fingerprint Recognition Approaches: A Review

Developing an algorithm for Fingerprint Recognition of Newborns and Toddlers based on Deep Neural Networks and Transfer Learning

Enhancing child safety with accurate fingerprint identification using deep learning technology

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