A New Hybrid Technique for Face Identification Based on Facial Parts Moments Descriptors

Hamandi, Shaymaa M.; Rahma, Abdul Monem S.; Hassan, Rehab F.

doi:10.30684/etj.v39i1b.1903

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Moments descriptors can be categorizing as Algebraic, Geometric and Orthogonal moments depending on the polynomial basis that used and amount of information that carry them. Face image moments are very useful for providing an elegant style for describing the face area through small number of descriptive and highly intuitive values [16].…”

Section: Traditional Face Recognition Systemmentioning

confidence: 99%

People Identification based on Geometric Face Features for Cloud Services

Hachim

2023

JECNAM

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Recently, the services provided by cloud computing play an important role in many areas of life. The security component represents the major challenge that hinders the adoption of cloud services in banking, healthcare and other transactions. In this paper, secure identification approach is proposed to use cloud services by recognizing authorized persons based on facial features. In the proposed approach, the face image is sent to the cloud side without any processing. When it is received in the cloud side, the image is processed and the geometric features are extracted using moment features extractor. The features of the face are used during the matching phase by using two different classifiers to make the final decision to grant authorization to access cloud services. The experimental results showed the efficiency and accuracy of the proposed approach. The performance is test using many facial images with online applications. Where an overall accuracy level of approximately 96% was obtained.

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Section: Traditional Face Recognition Systemmentioning

confidence: 99%

People Identification based on Geometric Face Features for Cloud Services

Hachim

2023

JECNAM

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“…The proposed technique analyzes the face detection method based on LBP, which is an appearance-based method. Usually, this type of method provides good results [22].…”

Section: Face Detection Phasementioning

confidence: 99%

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Ogla

Saeid

Shaker

2022

IJECE

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<p><span>The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.</span></p>

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“…Facial recognition technology has become an integral part of modern life, permeating various devices and systems, including cell phones, laptops, and surveillance equipment [1]. Its applications span security, access control, biometrics, and human-computer interaction [2] revolutionising different domains and enhancing user experiences [3] [4].…”

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confidence: 99%

Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

Alshiha,

Al-Neama,

Qubaa

2023

IJEER

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This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods. HFS seamlessly incorporates Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), and HOG. The primary aim of this algorithm is to autonomously identify a subset of the most distinctive features from the extracted feature pool, thus elevating classification accuracy, precision, recall, and F1-Score. By amalgamating these methodologies, the algorithm adeptly diminishes dimensionality while conserving pivotal features. Experimental trials on facial image datasets, CK+ and FERET, underscore the algorithm's supremacy in terms of accuracy and computational efficiency when contrasted with conventional linear techniques and even certain deep learning approaches. The proposed algorithm proffers an encouraging solution for real-world face recognition applications where precision and operational efficiency are of paramount significance.

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A New Hybrid Technique for Face Identification Based on Facial Parts Moments Descriptors

Cited by 4 publications

References 28 publications

People Identification based on Geometric Face Features for Cloud Services

People Identification based on Geometric Face Features for Cloud Services

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

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