Incremental methods in face recognition: a survey

Madhavan, Suresh; Kumar, Nitin

doi:10.1007/s10462-019-09734-3

Cited by 27 publications

(11 citation statements)

References 85 publications

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“…Locality preserving projection (LPP) algorithm [12] is an unsupervised dimensionality reduction algorithm, which mainly aims at dimensionality reduction of nonlinear data.…”

Section: Locality Preserving Projection (Lpp) Algorithmmentioning

confidence: 99%

Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images

2021

Scientific Programming

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In this paper, a novel approach for facial expression recognition based on sparse retained projection is proposed. The locality preserving projection (LPP) algorithm is used to reduce the dimension of face image data that ensures the local near-neighbor relationship of face images. The sparse representation method is used to solve the partial occlusion of human face and the problem of light imbalance. Through sparse reconstruction, the sparse reconstruction information of expression is retained as well as the local neighborhood information of expression, which can extract more effective and judgmental internal features from the original expression data, and the obtained projection is relatively stable. The recognition results based on CK + expression database show that this method can effectively improve the facial expression recognition rate.

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“…Locality preserving projection (LPP) algorithm [12] is an unsupervised dimensionality reduction algorithm, which mainly aims at dimensionality reduction of nonlinear data.…”

Section: Locality Preserving Projection (Lpp) Algorithmmentioning

confidence: 99%

Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images

2021

Scientific Programming

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“…Very recently, deep learning is of interest to researchers in the field of computer vision. In particular, convolutional neural networks (CNNs) have attracted a lot of attention due to their superior performance in many domains, such as face recognition [3][4][5], object detection and video classification [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Image Classification Using a Fully Convolutional Neural Network CNN

Lalaoui¹,

Abdelhak²,

Abderrahmane³

et al. 2022

MMEP

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This article reviews the field of image processing in recent years is enormously developed and it has been used in several specialties like medical, stand-alone, satellite and the purpose of this field is to improve image quality and extract information. Pneumonia has become in recent years a defective disease that affects the majorities of the population is especially the elderly and can sometimes put their lives in danger, in order to save human life early pneumonia diagnostic is necessary; in this work we have based on the detection and classification of patients with pneumonia from their chest x-ray. However, there are several areas where image classification is applied with success, in our work we have used deep learning based on the most common convolutional neural networks to make an image classification of pneumonia disease and to obtained good results and gave several advantages.

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“…In [5] the performance of some face recognition algorithms based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are compared. In [6] a survey of methods of face recognition is reported. To overcome the limits of face verification, face biometry is commonly used together with other biometries.…”

Section: Introductionmentioning

confidence: 99%

Dempster-Shafer-Based Fusion of Multi-Modal Biometrics for Supporting Identity Verification Effectively and Efficiently

Cuzzocrea

Mumolo

2021

2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)

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The purpose of this paper is to describe a novel fusion algorithm for multi-modal biometric identification. In this paper we describe the fusion of fingerprints and voice. This combination of biometrics is rarely used in verification systems although this biometric pair is simple to use and not too invasive. A framework for the combination of several data fusion algorithms is described. In this paper we use only two types of data fusion techniques, namely weighted sum and fuzzy system. Two independent identity decisions can be thus obtained, and from them two beliefs that the identity is verified can be derived. The two beliefs are combined using Dempster-Shafer's approach to obtain the final decision. The results are reported by ROC curves.

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Incremental methods in face recognition: a survey

Cited by 27 publications

References 85 publications

Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images

Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images

Image Classification Using a Fully Convolutional Neural Network CNN

Dempster-Shafer-Based Fusion of Multi-Modal Biometrics for Supporting Identity Verification Effectively and Efficiently

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