The Using of PCA, Wavelet and GLCM In Face Recognition System,  A Comparative Study

Al-Abaji, Manar Abdulkaream; Salih, Meaad Mohammed

doi:10.29196/jubpas.v26i10.1848

Cited by 7 publications

(6 citation statements)

References 2 publications

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“…Here the experiment was conducted on ORL and AR face databases. Al-Abaji and Salih [20] were proposed one-dimensional data reduction techniques namely, GLCM, PCA, DWT with Euclidian distance classifier for face recognition. In this work, the PCA features, HH segment band DWT features, and the GLCM of energy, contrast, correlation, and homogeneity features were considered for the experiment.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

Kumar

Mathivanan

2021

IJEECS

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In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.

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Section: Related Workmentioning

confidence: 99%

A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

Kumar

Mathivanan

2021

IJEECS

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“…Among the most popular statistical methods used to extract texture characteristics is the co-occurrence matrix and obtaining a number of traits based on mathematical equations in which the intensity distribution and relative placements of nearby pixels in an image are described [10], [11]. In this paper, four traits will be used as in the following equations:…”

Section: Co-occurrence Matrixmentioning

confidence: 99%

A Suggested System For Palmprint Recognition Using Curvelet Transform And Co-Occurrence Matrix.

alhadidi¹

2021

J.Edu.Sci.

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The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the curvelet transform and co-occurrence matrix to recognize a hand's palmprint. The suggested system is composed of several stages: in the first stage, the region of interest (ROI) was taken from a palmprint image, then in the second stage, the curvelet transform was applied to the (ROI) to get a blurred version of the image, and finally, unsharp masking process and sobel filtering were performed for edge detection. The third stage involves feature extraction using a co-occurrence matrix to obtain 16 features, while the fourth stage inclusion is the training and testing of the suggested approach. The algorithm ACO (ant colony optimization) has been adopted to evaluate the shortest path to the goal. CASIA PalmprintV dataset of 100 people (60 male and 40 female) was used in proposed work to rate the performance of the proposed system. ARR and EER metrics have been adopted to assess the performance of the proposed system. The experimental results showed a very high recognition rate (ARR) that reaches 100% for the right hand of a male and the left hand of a female. The overall accuracy rate (ARR) reaches 98.5% and EER equals 0.015.

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“…The study showed the third level is the best level of disassembly [14]. In the other work [15], a descriptor is obtained by projecting a face as an input on a eigenface space, then the descriptor is fed as an input to each object's pre-trained network. They determine and report the maximum output if it passes the threshold previously established for the recognition system.…”

Section: Related Workmentioning

confidence: 99%

Improving face recognition by artificial neural network using principal component analysis

2020

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The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person's faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third and fourth models were trained using the same features but using Elman neural network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33, 98.80) with (40, 50) features respectively, while Elman gives (98.33, 95.14) for with (40, 50) features respectively.

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The Using of PCA, Wavelet and GLCM In Face Recognition System, A Comparative Study

Cited by 7 publications

References 2 publications

A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

A Suggested System For Palmprint Recognition Using Curvelet Transform And Co-Occurrence Matrix.

Improving face recognition by artificial neural network using principal component analysis

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