Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Prasetyo, Mochammad Langgeng; Wibowo, Achmad Teguh; Ridwan, Mujib; Milad, Mohammad Khusnu; Arifin, Sirajul; Izzuddin, Muhammad; Setyowati, Rr Diah Nugraheni; Ernawan, Ferda

doi:10.3991/ijim.v15i10.20175

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…The loss function and evaluation metrics are calculated for this dataset after each epoch. (16) The results of our simulations shown in Table 1 below have demonstrated a remarkable performance recorded in both cases (ORL and UMIST) as shown in Table 1 below. In the case of the ORL database which represents the presence of variations in contrast, lighting, and occlusion (glasses, sling ...) our approach recorded an accuracy rate of up to 99.50% which is the best score recorded among the other techniques while a value of 0.07 was recorded by our model as the precision of the loss function which indicates that our model has succeeded in minimizing the value of the loss function.…”

Section: Resultsmentioning

confidence: 65%

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A combined method based on CNN architecture for variation-resistant facial recognition

Benradi,

Chater,

Lasfar

2023

Int. j. electr. comput. eng. syst. (Online)

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Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.

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Section: Resultsmentioning

confidence: 65%

“…extraction and classification such as [16][17][18]. CNNs are a category of deep neural networks used mainly in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

A combined method based on CNN architecture for variation-resistant facial recognition

Benradi,

Chater,

Lasfar

2023

Int. j. electr. comput. eng. syst. (Online)

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“…The rice plant dataset is used to fine-tune multiple transfer learning models, including InceptionV3, Resnet50, Vgg16, Mobilenet, and Xception. A dense layer, softmax layer, and pooling layer were added to the pre-trained model classification layer [27]. The models were trained using 30 epochs and a batch size of 32, cross-entropy, and Adam optimizer were used to test and validate each model.…”

Section: Methodsmentioning

confidence: 99%

Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models

Sobhana

Vallabhaneni

Vasireddy

et al. 2022

Int. J. Interact. Mob. Technol.

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India is an agricultural country, and farming is the most common occupation among Indians. Rice is a vital crop in the agricultural industry. Productivity has been declining for almost a decade. There are several causes for this, including fragmented land holdings, Indian farmer illiteracy, a lack of decision-making capacity in selecting excellent seeds, manure, and irrigational infrastructure. One of the major reasons for rice crop failure is due to malnutrition. Rice, maybe in particular, lacking in nutrients such as potassium, nitrogen, and phosphorus. Nutrient deficiency detection in crops is necessary to plan further actions to enhance yield. Most studies have relied on the use of transfer learning models for agricultural uses. Ensembling of different transfer learning techniques has the ability to greatly increase the predictive model’s performance. Five transfer learning architectures InceptionV3, Xception, VGG16, Resnet50, and MobileNet are all taken into account, and their different ensemble models are used to perform deficiency detection in rice plants. The mobile application was created as a user-friendly interface to assist farmers. The accurate diagnosis of these nutritional deficiencies and recommendation of fertilizer could aid farmers in providing correct plant intervention.

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“…It is important to notice that the second most largest category goes to private datasets with 34 papers (Zhou et al, 2018;Ara et al, 2017;Phankokkruad, 2018;Gilani and Mian, 2018;Khan et al, 2019b;Qin et al, 2019;Liu et al, 2019;Peng et al, 2019;Mangal et al, 2020;Lv et al, 2020;Perti et al, 2020;Kim et al, 2017;Irjanto and Surantha, 2020;Arafah et al, 2020;Prasetyo et al, 2021;Moon et al, 2017;Chandran et al, 2018;Yang et al, 2018;Son et al, 2020;Alhanaee et al, 2021;Khan et al, 2020;Nakajima et al, 2021;Talahua et al, 2021;He and Ding, 2023;Karlupia et al, 2023;Bussey et al, 2017;Li et al, 2022;Filippidou and Papakostas, 2020;Bussey et al, 2017;Singh et al, 2022;Setio Aji et al, 2022;Wang et al, 2022;Lestari et al, 2021) creating their own datasets for testing. There are advantages and disadvantages to developing and utilizing private face image datasets for CNN-based face recognition.…”

Section: Assessment Of Q3: What Type Of Cnn Model Is Most Commonly Us...mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. By analyzing 150 research papers, we investigate major publication channels, temporal trends, prevalent CNN models, primary data sets, accuracy levels, primary research focuses, and future prospects for improving CNN-based facial recognition. A major focus is placed on identifying prevalent CNN architectures, techniques used for facial recognition and shedding light on the evolving landscape of CNN designs. Furthermore, we examine the datasets used for training and testing CNNs, and evaluate the accuracy levels achieved by these models. Lastly, we discuss future directions for enhancing CNN-based facial recognition, including addressing bias and fairness, improving robustness to environmental variations, privacy preservation, and exploring transfer learning and multimodal fusion. This paper serves as a valuable resource, summarizing major trends in CNN-based face recognition. It provides insights for researchers and practitioners, guiding future advancements in this rapidly evolving field.

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Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Cited by 9 publications

References 18 publications

A combined method based on CNN architecture for variation-resistant facial recognition

A combined method based on CNN architecture for variation-resistant facial recognition

Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

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