A Review of Human Face Detection in Complex Environment

Chen, Long; Liu, Yuling; Xin, Guojiang

doi:10.1007/978-981-15-8101-4_24

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Instead of comparing the value of each pixel with the value of the central pixel, the central symmetrical pixel pairs are compared. Therefore, the number of comparisons is reduced by half for the same number of neighboring pixels, and this idea has accelerated the face recognition process using LBP [18], [19].…”

Section: Local Binary Pattern Histogram (Lbph)mentioning

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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Section: Local Binary Pattern Histogram (Lbph)mentioning

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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“…Feature-based methods detect face region based on internal facial features as well as the geometrical relationship among them [ 22 ]. Contrary to knowledge-based methods, feature-based methods seek constant features as a means of detection.…”

Section: Related Workmentioning

confidence: 99%

A face recognition software framework based on principal component analysis

et al. 2021

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Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

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Human-Face Image Retrieving Based Texture Feature Extraction Method

Shaker¹

2022

2022 2nd Asian Conference on Innovation in Technology (ASIANCON)

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A Review of Human Face Detection in Complex Environment

Cited by 3 publications

References 24 publications

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

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

A face recognition software framework based on principal component analysis

Human-Face Image Retrieving Based Texture Feature Extraction Method

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