Real-time Multi-object Face Recognition Using Content Based Image Retrieval (CBIR)

Fachrurrozi, Muhammad; Saparudin, Saparudin; Erwin, Erwin; Mardiana, Mardiana; Badillah, Clara Fin; Erlina, Junia; Lazuardi, Auzan

doi:10.11591/ijece.v8i5.pp2812-2817

Cited by 6 publications

(4 citation statements)

References 9 publications

(11 reference statements)

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“…The faces multi image datasets came from social media, mobile phone and digital camera. In this experiment, the color space YCbCr percentage value with the image initial value detection before processing are 67.15%, 75.00%, and 64.58% have been reached [10].…”

Section: Literature Surveymentioning

confidence: 91%

Human face detection and recognition using contour generation and matching algorithm

T.S¹,

Baskar²,

Sateesh³

2019

IJEECS

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Face detection, monitoring of human face and recognition is a high-quality venture in safety, surveillance and regulation enforcement systems with greater accuracy. There are many motives at the back of in this trouble. Which include function of the human face; pose version, negative environmental conditions, terrible lightning and picture tilt etc,. So we must locate a few feasible solutions to clear up those issues by means of imposing a brand new system. Our proposed venture might be improving the issues on Face detection, monitoring of human face and recognition issues.

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Section: Literature Surveymentioning

confidence: 91%

Human face detection and recognition using contour generation and matching algorithm

T.S¹,

Baskar²,

Sateesh³

2019

IJEECS

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“…The distance recognition technique was applied on whole faces, and a ratio-based structural point and curve map was applied on the partial faces. In 2019, Fachrurrozi et al [11] presented a method to automate the search process that depended on mathematical calculations on images to specify the features of the face. The accuracy of that system was 96.2%.…”

Section: Review Of Existing Methodsmentioning

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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“…For two data points x and y in d-dimensional space, the Euclidean distance is the most common distance used for numerical data. The Euclidean distance is determined by comparing the proximity of the distance values of two variables, namely the test image and the reference image, in order to determine the closest distance value (Fachrurrozi et al, 2018). The result of the comparison calculation is the smallest value (the closest distance).…”

Section: Introductionmentioning

confidence: 99%

Webcam-Based Physical Distancing Prototype to Calculate Real-Time Distance by Applying Euclidean Distance

Rahmatillah

2023

JITE

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The Covid-19 virus has spread rapidly in Indonesia in recent years, significantly increasing the number of victims in the community. However, there is a lack of warnings for violators of physical distancing. This study aims to create a system as a solution to overcome the problems of implementing physical distancing protocols. Image capture media in the form of cameras were used to monitor human objects in crowded situations by tracking the movement of objects at regular physical distances. In this study, computer vision, image processing, and pattern recognition were used to construct the distance measurement between humans. Using a webcam by applying the Euclidean Distance and Haar Cascade methods, this system is intended to determine the distance between human-made objects. The results demonstrated that a digital image system employing the Haar Cascade and Eucludien Distance methods could make it easier to control the gathering point in a given area. Researcher has developed a software usage assistance feature for use with this application. Based on the findings of the study, the accuracy of this system was evaluated using 100 trials, with a succes rate of 100% since the system resulted correct notification in all trials. In addition, this digital image system is anticipated to assist developers and the general public in controlling gathering locations that do not adhere to established health protocols, allowing violations of physical separation to recognize their errors when captured on camera immediately.

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Real-time Multi-object Face Recognition Using Content Based Image Retrieval (CBIR)

Cited by 6 publications

References 9 publications

Human face detection and recognition using contour generation and matching algorithm

Human face detection and recognition using contour generation and matching algorithm

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

Webcam-Based Physical Distancing Prototype to Calculate Real-Time Distance by Applying Euclidean Distance

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