A Face Recognition System Based on Back Propagation Neural Network Using Haar Wavelet Transform and Morphology

Gautam, K. S.; Quadri, Nadira; Pareek, Abhinav; Choudhary, Surendra Singh

doi:10.1007/978-81-322-1817-3_10

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…This study extends the analysis and framework for the identification of human faces reported in previous studies [4,5,9], and [40,41] but uses a different approach to the classification of human skulls. The results from previous studies are summarized in Tab.…”

Section: Discussionmentioning

confidence: 65%

“…Krisshna et al [8] used transform domain feature extraction combined with feature selection to improve the accuracy of prediction. In contrast, Gautam et al [9] proposed image decomposition using Haar wavelet transforms through a classification approach in which the quantization transform and the split-up window of facial images were combined. Faces were classified with backpropagation neural networks and distinguished from other faces using feature extraction when considering a grayscale morphology.…”

Section: Contributions Of This Workmentioning

confidence: 99%

See 1 more Smart Citation

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Yuadi¹,

Artaria²,

Asyhari³

et al. 2021

Computers, Materials &Amp; Continua

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The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features. Each underlying facial bone exhibits unique characteristics essential to the face's physical structure that could be exploited for identification. Therefore, we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches. Using our proposed approach, we were able to achieve an accuracy of 92.3-99.5% in the classification of human skulls with mandibles and an accuracy of 91.4-99.9% in the classification of human skills without mandibles. Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

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Section: Discussionmentioning

confidence: 65%

Section: Contributions Of This Workmentioning

confidence: 99%

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Yuadi¹,

Artaria²,

Asyhari³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This paper presented modified eigenphases algorithms that improve the eigenphases algorithms previously proposed in (Gautam et al, 2014;Hou et al,2013). The performances of the proposed algorithms were evalua- Figure 10.…”

Section: Discussionmentioning

confidence: 99%

“…Among them, the eigenphases approach, which uses the phase spectrum (Zaeri, 2009), together with principal component analysis (PCA) and the support vector machine (SVM) (Benitez et al, 2011;Zaeri, 2009;Olivares et al, 2009;Benitez et al, 2012), appears to be a desirable approach because it provides recognition rates of over 95%. The use of other frequency transformations, such as the discrete cosine transform (Dabbaghchian et al, 2010;Ajit et al, 2014), discrete Gabor transform (Olivares et al, 2007;Thiyagarajan et al, 2010;Qin et al, 2012), discrete wavelet transform (Hu, 2011;Eleyan et al, 2008) and discrete Haar transform (Gautam et al, 2014), has also been proposed. These approaches, under controlled conditions, achieve recognition rates of over 90%.…”

Section: Introductionmentioning

confidence: 99%

Modifications to the Eigenphases Method for Face Recognition Based on SVM

Olivares-Mercado

Toscano-Medina

Sánchez-Pérez

et al. 2016

Ingeniería, Investigación y Tecnología

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This paper presents two modifications to the eigenphases method to increase its accuracy. In the first modification, called Local Spatial Domain Eigenphases (LSDE), the face image is first segmented into blocks of N × N pixels, whose magnitudes are normalized. These blocks are then concatenated before the phase spectrum estimation, and finally Principal Component Analysis (PCA) is used for dimensionality reduction. In the second modification, called Local Frequency Domain Eigenphases (LFDE), first the face image is segmented into blocks of pixels, whose pixels are normalized. The phase spectrum of each block is estimated independently. Next, the phase spectra of all the blocks are concatenated and then are applied to the PCA stage for dimensionality reduction. The proposed approaches are evaluated using open-set and closed-set face identification, as well as identity verification, using the "AR Face Database." The evaluation results show that the proposed modifications, using the Support Vector Machine as the classifier, perform fairly well under different illumination and partial occlusion conditions.

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“…Another way to solve the problem of illumination changes is the development of different high-performance methods to solve these kinds of changes such as the eigenphases approach [7][8][9][10][11]. Also, are useful some methods based on frequency transforms like discrete cosine transform [12][13][14], discrete Gabor transform [15][16][17], discrete wavelet transforms [18][19][20][21], and discrete Haar transform [22]. Additional methods that could be applied are the eigenfaces [23,24] which use the principal component analysis (PCA) [25,26], modular PCA-based face recognition methods [27], the Fisherfaces approach [28], and the Laplacianfaces [29].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Texture Descriptors

Olivares-Mercado¹,

Toscano-Medina²,

Sánchez-Pérez³

et al. 2018

From Natural to Artificial Intelligence - Algorithms and Applications

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In this chapter, the performance of different texture descriptor algorithms used in face feature extraction tasks are analyzed. These commonly used algorithms to extract texture characteristics from images, with quite good results in this task, are also expected to provide fairly good results when used to characterize the face in an image. To perform the testing task, an AR face database, which is a standard database that contains images of 120 people, was used, including 70 images with different facial expressions and 30 with sunglasses, and all of them with different illumination intensity. To train the recognition system from one to seven images were used for each person. Different classifiers like Euclidean distance, cosine distance, and support vector machine (SVM) were also used, and the results obtained were higher than 98% for classification, achieving a good performance in verification task. This chapter was also compared with other schemes, showing the effectiveness of all of them.

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A Face Recognition System Based on Back Propagation Neural Network Using Haar Wavelet Transform and Morphology

Cited by 5 publications

References 6 publications

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Modifications to the Eigenphases Method for Face Recognition Based on SVM

Face Recognition Based on Texture Descriptors

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