A shearlet transform based illumination invariant 2-D face recognition

Singh, Charushila R.; Patil, Hemprasad Yashwant

doi:10.1109/iceeot.2016.7755336

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In multivariate problem, shearlets allows to encode the efficient anisotropic features. Shearlets used for sparse approximation functions   f .The shearlet transform is used in other fields like mammogram classification [13], Face recognition technique [14] and satellite image denoising [15]. In this study, shearlet transform is used to decompose the skin cancer image features.…”

Section: A Shearlet Decompositionmentioning

confidence: 99%

Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier

Kumar¹,

Kumar²,

Gopalakrishnan³

2019

IJEAT

View full text Add to dashboard Cite

Development of abnormal cells in the skin is known as skin cancer or melanoma, which can spread other parts of the body. Melanoma rarely occurs in eye, mouth and intestines. In this study, the classification of melanoma using shearlet transform coefficients and naïve Bayes classifier is discussed. The melanoma images are decomposed by the shearlet transform. Then, from the shearlet coefficients, predefined number of (50, 75 and 100) coefficients are selected from the decomposed subbands. The selected subband coefficients are directly applied to the naïve Bayes classifier. Performance of skin cancer classification system is measured in terms of accuracy. Results show that a better classification accuracy of 90.5 % is achieved at 3rd level with 100 coefficients of shearlet transform and naïve Bayes classifier for skin image classification system.

show abstract

Section: A Shearlet Decompositionmentioning

confidence: 99%

Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier

Kumar¹,

Kumar²,

Gopalakrishnan³

2019

IJEAT

View full text Add to dashboard Cite

show abstract

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

Bhamare,

Patil

2023

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

Person identification using periocular images has emerged as a challenging scenario in efficient biometric analysis, particularly under less constrained environments. Accurate recognition is significant in rendering effective measures during the COVID-19 pandemic. In this research paper, the person identification process is performed based on a deep learning model. Several effectual methods have already been developed, but certain drawbacks still exist, like deteriorated image quality, high computational cost, increased error, less training ability, a requirement of high storage space and accuracy rate degradation. Hence, the proposed work introduces a Hybrid Optimal Dense Capsule network-based Periocular biometric system (HodCP) to conquer these demerits. The proposed work involves pre-processing, dimensionality reduction, hybrid feature extraction and Image matching. The pre-processing step is undertaken using Parabolic Contrast Enhancement (PCE) to balance the image contrast and enhance the image quality. Then the Two-Dimensional Principal Component Analysis (2D_PCA) is employed to minimize the image dimensionality. Deep features are extracted in the hybrid feature extraction process using Dense Convolutional-121 Capsule Network (DenseCapsNet). The net loss and hyperparameter tuning are performed through African Vultures Optimization (AVO) algorithm. Finally, image matching is performed using Weighted Distance Similarity (WDS), which identifies the similarity between the query image and a set of image samples based on the distance score. The simulation tool used for analyzing better performance is PYTHON. The data required to process the proposed work are collected from four benchmark datasets. The proposed work provides a better accuracy rate of CASIA-Iris-Mobile-V1.0 (99.01%), UBIPr (99.12%), Facemask detection dataset (98.67%) and Glasses versus without glasses dataset (98.83%), which is superior to the existing methods.

show abstract

Illumination invariant face recognition using contourlet transform and convolutional neural network

Hussain

Alotaibi

Qazi

et al. 2022

IFS

View full text Add to dashboard Cite

The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods.

show abstract

A shearlet transform based illumination invariant 2-D face recognition

Cited by 3 publications

References 21 publications

Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier

Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

Illumination invariant face recognition using contourlet transform and convolutional neural network

Contact Info

Product

Resources

About