Face identification using the magnitude and the phase of Gabor wavelets and PCA

Bellakhdhar, Faten; Bousselmi, Moncef; Abid, Mohamed

doi:10.1109/icmcs.2012.6320273

Cited by 4 publications

(3 citation statements)

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“…In face recognition the main issue is what features used to represent a face, the proposed algorithm extract 17 fiducial points i.e. automatically located by Active Appearance (AAMs) and characterized with Gabor wavelet analysis [14]. These features evaluated by a face database, Under some the result shows that the proposed algorithm is effective and robust.…”

Section: Gabor Wavelet Feature Based Approachmentioning

confidence: 88%

“…geometric distances and Gabor coefficient, the result shows that Gabor cofficent are more powerful than geometric distances. The facial feature vector is : V = [Dcenter_eye; Deye; Dinterior_eye; Dnose; Deye_nose; Dmouth; Dnose_ mouth] An Author [14] introduced a new methodology in the place of raster image, to enhance face recognition rate by fusing the phase and magnitude of Gabor's representations of the face as a new representation. For this, principal component Analysis (PCA) was used as a face recognition algorithm and Eigenface was used to extract the global information.…”

Section: Gabor Wavelet Feature Based Approachmentioning

confidence: 99%

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A Survey on Feature Extraction Techniques

Elavarasan¹,

Mani²

2015

IJIRCCE

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Face recognition is very important in computer vision. For human being it is easy to identify human face in any posture but it is not an easy task for systems. Feature extraction is useful technique for recognizing faces through systems. It is used for security, criminal records or identification, verification of person, etc. In face images there are variety of face posture. The question is, How to recognize the face? In this paper, discuss certain approaches for face recognition by using feature extraction along with the issues and the recommendations as to which approach is better and suitable.

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Section: Gabor Wavelet Feature Based Approachmentioning

confidence: 88%

Section: Gabor Wavelet Feature Based Approachmentioning

confidence: 99%

A Survey on Feature Extraction Techniques

Elavarasan¹,

Mani²

2015

IJIRCCE

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“…It is a good dimension reduction technique always used before a classification algorithm. GABOR filter was introduced by Dennis Gabor in 1946, then improved in 1980 by John G. Daugman[51]. This filter is used to extract useful local facial features from an image.…”

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confidence: 99%

Comparative study of machine learning algorithms for face recognition

Alagah Komlavi,

Chaibou,

Naroua

2024

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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Background: The fundamental need for authentication and identification of humans using their physiological, behavioral or biological characteristics, continues to be applied extensively to secure localities, property, financial transactions, etc. Biometric systems based on face characteristics, continue to attract the attention of researchers, major public and private services. In the literature, many methods have been deployed by different authors. The best performance must be found in order to be able to recommend the most effective method. So, the main objective of thisarticle is to make a comparative study of different existing techniques.Methods: A biometric system is generally composed of four stages: acquisition of facial images, preprocessing, extraction of characteristics and finally classification. In this work, the focus is on machine learning algorithms for classification. These algorithms are: Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), Logistic Regression (LR), Naive Bayesian Classification (NB: Naive Bayes’ Classifiers) and deep learning techniques such as Convolutional Neural Networks (CNN). The comparison criterion is the average performance, calculated using three performance measures: recognition rate, confusion matrix, and the Area Under Receiver Operating Characteristic (ROC) curve.Results: Based on this criterion, the performance comparison of selected machine learning algorithms, shows that CNN is the best, with an average performance of 100.00% On ORL face database. However, on the YALE database, classical algorithms such as artificial neural networks have obtained the best performances, the highest being a rate of 100%.Discussion: Deep learning techniques are very efficient in image classification as proven by the results on the ORL database. However, their inefficiency on YALE face database is due to the small size of this database which is inappropriate for some deep learning algorithms. But this weakness can be corrected by image augmentation techniques. The comparison of these results with existing state-of-the-art methods is nearly the same. Authors achieved performances of 94.82%, 95.79%, 96.15%, 96.44%, 97.27%, 98.52% and 98.95% for NB, KNN, RF, LR, ANN, SVM and CNN classifiers, respectively. Finally, in depth discussion, it is concluded that between all these approaches which are useful in face recognition, the CNN is the best classification algorithm.

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