Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

Faizabadi, Ahmed Rimaz; Zaki, Hasan Firdaus Mohd; Abidin, Zaima Azira Zainal; Husman, Muhammad Afif; Hashim, Nik Nur Wahidah Nik

doi:10.51662/jiae.v3i1.84

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…While for other classes, the model detects no errors or damage to other classes. The computational time performance of each model used for training is calculated in minutes and for testing in seconds, as can be seen in Figure 8 [28,29,30,31]. The computational time performance of each model used for training is calculated in minutes and for testing in seconds, as can be seen in Figure 8.…”

Section: Resultsmentioning

confidence: 99%

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Aziz,

Trisiawan,

Suyasmini

et al. 2023

Sinergi

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Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.

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Section: Resultsmentioning

confidence: 99%

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Aziz,

Trisiawan,

Suyasmini

et al. 2023

Sinergi

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show abstract

“…Referring to the background, this research designs a biometric system model for identity verification through the palm of the hand. The designed system uses K-NN classification [33,34,35,36] and GLCM texture features for feature extraction and MATLAB. Image matching based on the human palm matches the test image taken through the smartphone's IP camera directly with the training image in the dataset.…”

Section: Methodsmentioning

confidence: 99%

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Rochendi,

Silalahi,

Simanjuntak

2024

Sinergi

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This research designs a touchless fingerprint identification biometric system. This research problem stems from the user's need to change the conventional system to touchless in the hope of minimizing direct contact that can be scanned by someone who is not interested. This research aims to implement palm images in validating identity using GLCM (Gray-Level Co-occurrence Matrix) features with the K-Nearest Neighbours (K-NN) classification method in MATLAB. The identification process is divided into several stages: image acquisition, pre-processing, feature extraction with GLCM, and database matching using KNN classification. System testing uses the 10-fold cross validation method with 100 image samples (90 training images and 10 test images) that are tested in turn to calculate the average accuracy and analyze system performance. Furthermore, the test used GLCM angles (0o, 45o, 90o dan 135o) and K-NN with k values of 1, 5 and 7. The results showed the highest accuracy of 72% using an angle of 0° in GLCM and k=1 and the lowest at an angle of 90o and k=7 in K-NN. The advantage of this design is the recognition of the identity of the fingerprint owner in real time.

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“…The collected data, encompassing various forms of information, including the temperature value of the users, has been meticulously documented utilizing the MySQL webserver database. MySQL, a renowned database management system widely recognized for its capacity to house extensive volumes of data, is employed in the present research [22] [23]. Figure 5 visually represents the registration data and temperature recording table hosted on MySQL.…”

Section: Database Creationmentioning

confidence: 99%

Facial Recognition and Thermal Imaging: A Cost-Effective Solution for Covid-19 Detection

Embong,

Ambotang,

Abdul Hamid

2023

JIAE

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The global pandemic induced by the 2019 Novel Coronavirus Disease (Covid-19) has posed significant challenges for nations across the globe. Given the pandemic's pervasive nature, there is an emerging demand for a dependable tool capable of identifying individuals exhibiting fever, a primary symptom of Covid-19 infection. To address this, utilizing facial recognition technology in conjunction with temperature measurement has been widely embraced within various infrastructures such as residential buildings and office spaces. This research proposes the adoption of a system capable of recognizing human faces while simultaneously monitoring individual temperatures. This is achieved through the utilization of Python and open-source libraries such as OpenCV and NumPy to develop an effective facial identification system. Furthermore, this research suggests leveraging the capabilities of a cost-effective AMG8833 thermal imaging camera to measure human body temperature. The thermal image, reflecting the individual's body temperature, is displayed on the Node-RED dashboard, a platform based on Internet of Things (IoT) technology. Should the temperature reading of an individual exceed 37.5 degrees Celsius, the system is designed to activate an alarm and dispatch notifications via an administrative mobile application. All pertinent information regarding the individual is securely stored within a MySQL webserver database. A comparative analysis reveals that the proposed system provides nearly 95% cost reduction when compared against commercial alternatives such as the Flir C3, with the added advantage of image recognition capabilities.

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Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

Cited by 6 publications

References 27 publications

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Facial Recognition and Thermal Imaging: A Cost-Effective Solution for Covid-19 Detection

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