Hybrid techniques to improve face recognition based on features extraction methods and Haar discrete wavelet transformation

Abdul-Jabbar, Isra’a Abdul-Ameer; Yakoob, Zainab Ali

doi:10.29304/jqcm.2018.10.2.372

Cited by 2 publications

(1 citation statement)

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“…It remains difficult for real-world implementations, as faces are recognized and remembered. [18,19] In this paper will explore how to face detection work using technique Dlib HOG based. The face detection system that presented consists of four steps include:…”

Section: Face Detection Systemmentioning

confidence: 99%

Detection of Human Faces Covered with disguise and Makeup

Al-ghanim

Al-juboori

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Face detection is kind of the identification. When we look at someone's face, we can get information like his or her gender and age. Face detection research has exploded in popularity during the last few decades. Starting with algorithms that can detect faces in constrained environments, today's face detection systems can attain extremely great accuracies at the large scale unconstrained facial datasets. While new algorithms continue to increase performance, the majority of face detection systems are vulnerable to failure when subjected to disguise and cosmetics alterations, which is one of the most difficult covariates to overcome. In this article, the database of disguised and makeup faces (DMFD) is employed. In order to address this issue, we detected the location and size of the facial in the image by using Histogram of Oriented Gradients (HOG) + Linear SVM Machine Learning detector on the Disguise and makeup face database (DMFD).This approach is effective and can detect any disguise and makeup faces in the complex background and illumination variation. The results shows the effectiveness of the face detection system on a database (DMFD) and it provided better results of (99.3%).

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Section: Face Detection Systemmentioning

confidence: 99%

Detection of Human Faces Covered with disguise and Makeup

Al-ghanim

Al-juboori

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Face recognition based on curvelets, invariant moments features and SVM

Ghazal

Abdullah

2020

TELKOMNIKA

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Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production.

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Hybrid techniques to improve face recognition based on features extraction methods and Haar discrete wavelet transformation

Cited by 2 publications

References 0 publications

Detection of Human Faces Covered with disguise and Makeup

Detection of Human Faces Covered with disguise and Makeup

Face recognition based on curvelets, invariant moments features and SVM

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