Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints

Kruthi, R; Patil, Awanikumar P.; Gornale, Shivanand S.

doi:10.26438/ijcse/v7i1.2229

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Additionally, LPQ can discriminate phase information from an image without losing the data at high frequencies. Here, the Discrete Fourier Transform is applied on the entire image linearly by considering image patch for the extraction of local phase information [20]. Taking into account linear filtering technique based on weighted sum of the image, phase of local frequency transform FT(u,v) is calculated in N x N neighborhood for every pixel in image M(i,j).…”

Section: Local Phase Quantization (Lpq)mentioning

confidence: 99%

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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In this paper, the Osteoarthritis (OA) analysis in knee radiographic images using artificial neural networks (ANN) is considered. In Osteoarthritis, mobility is restricted and bones rub each other causing extreme pain in knee due to cartilage disintegration. The cartilage destruction is minimal in the initial stage of OA. It is observed that a small number of researchers have implemented identification and grading of Osteoarthritis utilizing their own datasets for experimentation. However, there is still need of automatic computer aided techniques to detect Osteoarthritis for early recognition. In this work, a dataset of 1650 radiographic images of knee joints of OA patients are collected from different hospitals and have been annotated by two different orthopedic surgeons as per the Kellgren and Lawrence (KL) grading system. To automate this grading procedure, the local phase quantization and multi-block projection profile features are computed from the images and then presented to artificial neural network to classify the images based on the KL grading of the severity of the disease. The classification accuracy of 98.7% and 98.2% with reference to surgeon-1 and surgeon-2 opinions, respectively, is achieved.

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Section: Local Phase Quantization (Lpq)mentioning

confidence: 99%

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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“…Gender classification plays an important role in several face application scenarios such as surveillance, human computer interaction, contentbased searching and indexing. In general, different biometric traits are used to predict the gender of individuals [1][2][3][4][5][6][7][8][9][10]. The focus of this study is on gender classification using facial information in images.…”

Section: Introductionmentioning

confidence: 99%

“…The classification of gender using fingerprint biometric is proposed in [6] using a fusion of LBP and LPQ feature extractors and SVM classifier. The focus of [7] is on face-ocular multimodal biometric systems for a person gender prediction.…”

Section: Introductionmentioning

confidence: 99%

Gender Classification through Fusion of Holistic and Region-based Facial Patterns

Eskandarı

2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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In this study, a robust gender prediction system is proposed to fuse global and regional facial representations through score and feature level fusion. In order to extract facial features for gender classification, Binarized Statistical Image Features (BSIF) approach is applied on holistic and regional features of face images. The extracted features are then concatenated to combine the region-based information at feature level fusion. Then the optimized sub-set of features is selected using Particle Swarm Optimization (PSO) method. Finally, the holistic and regional features are combined at score level fusion to produce the final set of scores for gender classification. This study applies Weighted Sum (WS) rule strategy for score level fusion. The experimental results are performed on Multiple Biometric Grand Challenge (MBGC) and CASIA-Iris-Distance databases with consideration of subject-disjoint training and testing evaluation to testify the validity of the proposed gender classification system. The experimental results of the study demonstrate the success of the proposed scheme for gender prediction.

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Description method of Illumination invariant image features

Gao

Shan

Liu

et al. 2020

Signal Processing: Image Communication

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Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints

Cited by 4 publications

References 4 publications

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gender Classification through Fusion of Holistic and Region-based Facial Patterns

Description method of Illumination invariant image features

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