A Novel Design of Dyadic db3 Orthogonal Wavelet Filter Bank for Feature Extraction

Samantaray, Aswini K.; Rahulkar, Amol D.; Edavoor, Pranose J.

doi:10.1007/s00034-021-01723-8

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…There are several fingerprint databases such as MSU-ITF, VaxTrac, CMBD [30] and NITG [24,31] The performance of DCT domain features is checked for children"s fingerprints. The DCT is applied on cropped fingerprint images and standard deviation is calculated for a group of DCT coefficients.…”

Section: A Children Fingerprint Databasementioning

confidence: 99%

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

Kamble¹,

Dale²,

Bairagi³

2023

IJACSA

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Fingerprint biometric as an identification tool for children recognition was started in the late 19 th century by Sir Galton. However, it is still not matured for children as adult fingerprint identification even after the span of two centuries. There is an increasing need for biometric identification of children because more than one million children are missing every year as per the report of International Centre of missing and exploited children. This paper presents a robust method of children identification by combining Discrete Cosine Transform (DCT) features and machine learning classifiers with Deep learning algorithms. The handcrafted features of fingerprint are extracted using DCT coefficient's mid and high frequency bands. Gaussian Naïve Base (GNB) classifier is best fitted among machine learning classifiers to find the match score between training and testing images. Further, the Transfer learning model is used to extract the deep features and to get the identification score. To make the model robust and accurate score level fusion of both the models is performed. The proposed model is validated on two publicly available fingerprint databases of children named as CMBD and NITG databases and it is compared with state-of-the-art methods. The rank-1 identification accuracy obtained with the proposed method is 99 %, which is remarkable compared to the literature.

show abstract

Section: A Children Fingerprint Databasementioning

confidence: 99%

“…8. (a) Right thumb, (b) left thumb of CMBD [30] and (c) Right thumb (d) left thumb of NITG database[24,31]…”

mentioning

confidence: 99%

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

Kamble¹,

Dale²,

Bairagi³

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Point cloud feature extraction has become a research hotspot in 3D digital geometry processing techniques. This technology is widely used in sectors such as industrial design [15,26], medical research [10,28], shape recognition [20], spatiotemporal analysis [5,13], and digital protection of cultural relics [27,31]. High-quality feature extraction can provide strong support for subsequent point cloud registration, splicing, and surface reconstruction [18,35].…”

Section: Introductionmentioning

confidence: 99%

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

Jia

Pang

Xie

2022

Circuits Syst Signal Process

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The feature extraction of a point cloud fragment model is the basis of fragment splicing, which provides the technical support for research on the segmentation, splicing, and restoration of fragment surfaces. High-quality feature extraction, however, is a complicated process due to the diversity of the surface information of a fragment model. For this subject, a high-efficient point cloud feature extraction method was proposed to address a new method for extracting feature lines. First, the projection distance feature of the point cloud model was calculated to identify the potential feature points. Furthermore, the local information of the possible feature points was used to construct the adaptive neighborhoods for identifying the feature points based on neighborhoods of the model. The clustering fusion of the feature points was proposed according to the discrimination threshold values of the feature points. Finally, the Laplace operator was utilized to refine and connect the feature points to form smooth feature lines. The experimental results showed that the proposed method was automatic, highly efficient, and with good adaptability that could effectively extract the detailed features and construct the complete feature lines. Moreover, results showed that the provided framework could extract the features of simple structure models and be feasible to a certain extent for fragment models with abundant features.

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A Novel Design of Dyadic db3 Orthogonal Wavelet Filter Bank for Feature Extraction

Cited by 2 publications

References 29 publications

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

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

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

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