Comparison of different PCA based Face Recognition algorithms using Genetic Programming

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

doi:10.1109/istel.2010.5734132

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…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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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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“…In this section, we provide an overview of the previous studies on anomaly detection task. Unsupervised learning-based methods [6,7,8] take advantage of unlabeled data and can thus be adapted for the anomaly detection task. Several deep anomaly detection methods use the deep convolutional autoencoder (AE) [9,10].…”

Section: Related Workmentioning

confidence: 99%

Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays

Spahr¹,

Bozorgtabar²,

Thiran³

2021

Preprint

Self Cite

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Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large number of annotations by radiologists, which is often prohibitively very time-consuming to acquire. Moreover, supervised systems are tailored to closed set scenarios, e.g., trained models suffer from overfitting to previously seen rare anomalies at training. Instead, our approach's rationale is to use task agnostic pretext tasks to leverage unlabeled data based on a cross-sample similarity measure. Besides, we formulate a complex distribution of data from normal class within our framework to avoid a potential bias on the side of anomalies. Through extensive experiments, we show that our method outperforms baselines across unsupervised and self-supervised anomaly detection settings on a real-world medical dataset, the MURA dataset. We also provide rich ablation studies to analyze each training stage's effect and loss terms on the final performance.

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“…GP uses an iterative process to gradually improve the quality of solutions by undergoing a set of genetic operators, such as crossover and mutation [Zhao et al 2003]. Bozorgtabar et al [2010] have used GP to cluster the features of face images. The features have been extracted by two principal component analysis (PCA)-based FR algorithm,s namely, 2DPCA and MLPCA.…”

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%

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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Comparison of different PCA based Face Recognition algorithms using Genetic Programming

Cited by 10 publications

References 9 publications

An evolutionary-based image classification approach through facial attributes

An evolutionary-based image classification approach through facial attributes

Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays

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

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