A Genetic Programming approach to face recognition

Bozorgtabar, Behzad; Noorian, Farzad; Rad, Gholam Ali Rezai

doi:10.1109/ieeegcc.2011.5752477

Cited by 2 publications

(6 citation statements)

References 6 publications

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“…Using GP on its own to solve the FR problem is reported to be not suitable since the training time and computational overhead are larger than those of the other approaches. On that basis, Bozorgtabar et al [2011] solved this problem by using a leveraging algorithm along with GP, which will significantly increase the RR. GA can recognize the face images within a short period of time.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…The authors have concluded that using the GP algorithm on its own is not preferable because of its long training time compared to other methods. In their recent work, Bozorgtabar et al [2011] applied leveraging GP to the FR system. PCA is utilized to extract the most representative features of the image, thus providing dimensionality reduction, and then the GP algorithm is applied in order to classify the images.…”

Section: Genetic Programming (Gp)mentioning

confidence: 99%

“…However, an RR of 92.5% is reported by applying the Leave-One-Out approach. Most recently, Ibrahem et al [2013] [Bozorgtabar et al 2010] ORL 63.5% 67.5% Leveraged GP [Bozorgtabar et al 2011] ORL 91.5% 92.5% GP [Ibrahem et al 2013] ORL 76% 98% GNP-PCA [Zhang et al 2011b] Yale-B 76% GNP-MAS [Zhang et al 2011a] Yale-B 78.07%…”

Section: Genetic Programming (Gp)mentioning

confidence: 99%

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The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

et al. 2015

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An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Genetic Programming (Gp)mentioning

confidence: 99%

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The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

et al. 2015

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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%

“…The low detection rates ranging from 63.5% to 67.5% of the GP-based framework was increased when it was hybridized with a leveraging method. This work was expanded in [19] in which the same framework as [17,18] was used. In the expanded study, the contribution of different PCA's such as two-dimensional PCA, multilinear PCA with respect to the detection rate was examined.…”

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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A Genetic Programming approach to face recognition

Cited by 2 publications

References 6 publications

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

An evolutionary-based image classification approach through facial attributes

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