A Genetic Programming-PCA Hybrid Face Recognition Algorithm

Bozorgtabar, Behzad; Rad, Gholam Ali Rezai

doi:10.4236/jsip.2011.23022

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…The other 'experimentally exhaustive' work that relies on the GP was proposed for face recognition task in [17,18]. Similar to [16], a separate modal for every class, i.e., person, was evolved through the proposed GPbased framework.…”

Section: Related Workmentioning

confidence: 99%

An evolutionary-based image classification approach through facial attributes

Yılmaz¹,

Zalluhoglu²

2021

Turk J Elec Eng & Comp Sci

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With the recent development of the technology, it is seen that there is a significant increase in the studies on the analysis of human faces. Through the automatic analysing of the faces, it is possible to know the gender, emotional state, and even the identity of the people from an image. Of them, identity or face recognition has became the most important task which has been studied for a long time now as it is crucial to take measurement for public security, credit card verification, criminal identification, and the like. In this study, we have proposed an evolutionary-based framework that relies on genetic programming algorithm to evolve a binary-and multi-label image classifier program for gender classification, facial expression recognition, and face recognition tasks. The performance of the evolved programs has been compared with convolutional neural network, one of the most popular deep learning algorithms. The comparative results show that the proposed framework could better performance than the competitor algorithm. Therefore, it has been introduced to the research community as a new binary-and multi-label image classifier framework.

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Section: Related Workmentioning

confidence: 99%

An evolutionary-based image classification approach through facial attributes

Yılmaz¹,

Zalluhoglu²

2021

Turk J Elec Eng & Comp Sci

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“…Further, B. Bozorgtabar and G. Rad [11] proposed a technique for face recognition based on genetic programming. Firstly, the principal component analysis (PCA) method is used to extract features from the images.…”

Section: A Review Of Related Workmentioning

confidence: 99%

“…Secondly, genetic programming is used to classify image groups and a leveraging method is used to improve the results. Both of eigenface method in [18] and recognition method in [11] was tested on ORL database of faces. So, these two methods are used in the comparison with the proposed method.…”

Section: A Review Of Related Workmentioning

confidence: 99%

“…Bozorgtabar and G. Rad [11] applied their proposed technique on ORL database. They reported a recognition rate of 63.5% at 5 training images and 67.5% at 9 training images by using genetic programming, whereas they reported a recognition rate of 91.5% at 5 training images and 92.5% at 9 training images by using a leveraged genetic programming.…”

Section: Figure 5: Three Different Face Imagesmentioning

confidence: 99%

“…Furthermore, it can automatically create a general solution to a problem in the form of a parameterized topology [10]. It has never been used in face recognition domain although it has been used in classification and pattern recognition problems [11].…”

Section: Introductionmentioning

confidence: 99%

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Genetic Programming based Face Recognition

Ibrahem¹,

Nasef²,

Emam³

2013

IJCA

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Face recognition is a high-dimensional pattern recognition problem. It has rapidly evolved and has become very popular in recent years. In this paper, an efficient technique for face recognition based on genetic programming is proposed. Genetic programming is an evolutionary computation technique that automatically solves problems without having to tell the computer explicitly how to do it. Features extracting is one of the most important steps in this technique. The main goal of this paper is to answer the question "Who am I?" Further, the proposed technique is not affected by face recognition aspects such as lighting condition, varying facial expression, and varying pose. The results demonstrate that the proposed technique can obtain better performances than other existing face recognition techniques. General TermsPattern Recognition (Face Recognition). KeywordsFace recognition, genetic programming, and geometric feature based method.

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