Automatic Face Recognition Using Multi-Algorithmic Approaches

Zakariya, Syed Mohammad; Ali, Rashid; Lone, Manzoor Ahmad

doi:10.1007/978-3-642-22606-9_49

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…There is diversity in the images of different subjects like a subject has beard, glasses, moustaches etc. Figure.1 shows the flowchart of a PCA based face recognition system [9]. Preliminarily some preprocessing task is done such as resizing the image size.…”

Section: Orl Face Databasementioning

confidence: 99%

Comparative Analysis of Face Recognition Based on Significant Principal Components of Pca Technique

Lone¹

2019

IJCET

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Face recognition systems have been emerging as acceptable approaches for human authorization. Face recognition help in searching and classifying a face database and at a higher level help in identification of possible threats to security. In face recognition problem, the objective is to search a face in the reference face database that matches a given subject. The task of face recognition involves the extraction of feature vectors of the human face from the face image for differentiating it from other persons [6]. In this work, the comparative analysis is done based on the varying number of highly significant principal components (Eigenvectors) of PCA for face recognition. Experimental results show a small number of principal components of PCA are required for matching. PCA technique is a statistical technique, it reduces the dimension of the search space that best describes the images.

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Section: Orl Face Databasementioning

confidence: 99%

Comparative Analysis of Face Recognition Based on Significant Principal Components of Pca Technique

Lone¹

2019

IJCET

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“…Mian et al have presented 2D and 3D multimodal hybrid face recognition algorithm and tested its performance on the FRGC v1.0 dataset [4]. Zakariya et al have presented a multialgorithmic based approach using PCA and discrete cosine transformation (DCT) for personal identification and proved its usefulness [5]. Kar et al have developed a multialgorithmic based face recognition system, harnessing the combination of gray level 2 Journal of Electrical and Computer Engineering statistical correlation method with PCA or DCT methods, in order to intensify the performance of the systems [6].…”

Section: Introductionmentioning

confidence: 99%

Multialgorithmic Frameworks for Human Face Recognition

Shyam

Singh

2016

Journal of Electrical and Computer Engineering

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This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment. We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods. Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance. The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pattern (LBP), (ii) Fisherfaces and LBP, (iii) Eigenfaces and augmented local binary pattern (A-LBP), and (iv) Fisherfaces and A-LBP. The matching scores of these multialgorithmic frameworks are processed using different normalization techniques whereas their performance is evaluated using different fusion strategies. The robustness of proposed multialgorithmic frameworks of face recognition system is tested on publicly available databases, for example, AT & T (ORL) and Labeled Faces in the Wild (LFW). The experimental results show a significant improvement in recognition accuracies of the proposed frameworks of face recognition system in comparison to their individual methods. In particular, the performance of the multialgorithmic frameworks combining face recognition methods with the devised face recognition method such as A-LBP improves significantly.

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