Technique of Face Recognition Based on PCA with Eigen-Face Approach

Tatepamulwar, C. B.; Pawar, V. P.; Khamitkar, Santosh; Fadewar, H. S.

doi:10.1007/978-981-13-1513-8_92

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Then the minimum value of the auxiliary function at this value is taken as the value of the next base matrix. If we continue to use Equation (12) based on the found auxiliary function, we can satisfy Equation (13). Thus, we can prove that J is an non-increasing function and it is convergent, and the updating rule can be derived as Equation (17).…”

Section: New Iteration Rulementioning

confidence: 95%

“…The purpose is that face sample features can be easily classified in the new subspace [11]. PCA (Principal Component Analysis) is the most popular subspace method [12], and the PCA-based "eigenfaces" extraction technology is widely used in face feature extraction [13,14]. Additionally, for any face image, it can be represented by the combination of feature face images, and the feature vector is the corresponding relationship coefficient of feature face combination.…”

Section: Introductionmentioning

confidence: 99%

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Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Zhou

2019

Symmetry

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Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.

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Section: New Iteration Rulementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Zhou

2019

Symmetry

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An improved DFA based kernel ensemble learning machine using local feature representations for face recognition

Kavitha¹,

Soundar²,

Kumar³

2021

IFS

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In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images.

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Technique of Face Recognition Based on PCA with Eigen-Face Approach

Cited by 2 publications

References 3 publications

Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

An improved DFA based kernel ensemble learning machine using local feature representations for face recognition

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