Face recognition using fusion of PCA and LDA: Borda count approach

Borade, Sushma Niket; Deshmukh, Ratnadeep R.; Ramu, Sivakumar

doi:10.1109/med.2016.7536065

Cited by 23 publications

(9 citation statements)

References 13 publications

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“…In fact, in other pattern recognition areas, there have been some similar studies [123][124][125]. These studies have shown that a dimensionality reduction achieves an excellent performance in face recognition, and sparse processing is more effective in image processing and audio and video identification.…”

Section: Optimization Of Network Structure (1) Reconfiguration Orientmentioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.

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Section: Optimization Of Network Structure (1) Reconfiguration Orientmentioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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“…At present, facial feature extraction methods mainly include the appearance and geometry feature extraction, deformation and motion feature extraction, global and local feature extraction. Appearance features include image density, edge, texture and some more distinctive features, and the common appearance-based feature extraction methods include Local Binary Pattern (LBP) method [1], Linear Discriminant Analysis (LDA) [2] and directional gradient histogram method [3]. The appearance feature extraction methods are usually simple and direct, and can be fused with other methods to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

“…Such as in literature [1], the LBP-based feature is fused with the color information feature, and can measure the similarity of color images with rich color information. Literature [2] fused the LDA and principal component analysis methods at rank level using borda count method, thus the recognition accuracy over individual face representations is significant improved. Geometric method of feature extraction can recognize facial expressions by measuring facial geometric features, such as facial reference points, distance and curvature of variable significant areas, etc.…”

Section: Introductionmentioning

confidence: 99%

Facial Feature Recognition based on ASNMF Method

2019

KSII TIIS

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Since Sparse Nonnegative Matrix Factorization (SNMF) method can control the sparsity of the decomposed matrix, and then it can be adopted to control the sparsity of facial feature extraction and recognition. In order to improve the accuracy of SNMF method for facial feature recognition, new additive iterative rules based on the improved iterative step sizes are proposed to improve the SNMF method, and then the traditional multiplicative iterative rules of SNMF are transformed to additive iterative rules. Meanwhile, to further increase the sparsity of the basis matrix decomposed by the improved SNMF method, a threshold-sparse constraint is adopted to make the basis matrix to a zero-one matrix, which can further improve the accuracy of facial feature recognition. The improved SNMF method based on the additive iterative rules and threshold-sparse constraint is abbreviated as ASNMF, which is adopted to recognize the ORL and CK+ facial datasets, and achieved recognition rate of 96% and 100%, respectively. Meanwhile, from the results of the contrast experiments, it can be found that the recognition rate achieved by the ASNMF method is obviously higher than the basic NMF, traditional SNMF, convex nonnegative matrix factorization (CNMF) and Deep NMF.

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“…The process of face recognition can be divided into two sections, first is the process of salient feature extraction and second is the comparison of the extracted feature by a number of methods [4]. One of these well-known feature extraction method is Principle Components Analysis (PCA) [5]- [8]. PCA is a widely-known technique for feature extraction and data representation.…”

Section: Introductionmentioning

confidence: 99%

Face recognition system based on principal components analysis and distance measures

Goyal¹,

Meenpal²

2018

IJET

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Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.

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Face recognition using fusion of PCA and LDA: Borda count approach

Cited by 23 publications

References 13 publications

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Facial Feature Recognition based on ASNMF Method

Face recognition system based on principal components analysis and distance measures

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