Literature Study of Face Recognition using The Viola-Jones Algorithm

Hirzi, M. Fakhrul; Efendi, Syahril; Sembiring, Rahmat Widia

doi:10.1109/aims52415.2021.9466010

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…The culmination of this preprocessing stage was marked by the implementation of bounding box techniques, instrumental in demarcating and delineating the detected facial entities [3]- [5]. This process was conducted to isolate the critical facial regions.…”

Section: B Data Preprocessingmentioning

confidence: 99%

Learning Rate Analysis for Pain Recognition Through Viola-Jones and Deep Learning Methods

Raihan Islamadina,

Khairun Saddami,

Fitri Arnia

et al. 2024

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Deep learning is growing and widely used in various fields of life. One of which is the recognition of pain through facial expressions for patients with communication difficulties. Viola-Jones is a simple algorithm that has real-time detection capabilities with relatively high accuracy and low computational power requirements. The learning rate is a significant number that has an impact on the deep learning result. This study recognized pain using the Viola-Jones and deep learning methods. The dataset used was a thermal image from the Multimodal Intensity Pain (MIntPAIN) database. The steps taken consisted of segmentation, training, and testing. Segmentation was conducted using the Viola-Jones method to get the significant area of the face image. The training process was carried out using four deep learning benchmarks model, which were DenseNet201, MobileNetV2, ResNet101, and EfficientNetb0. Besides that, deep learning has a very important number to determine that is learning rate, which impact the deep learning results. There were five learning rates, which were 10-1, 10-2, 10-3, 10-4, and 10-5. Learning rate values were then compared with four deep models learning to obtain high accuracy results in a short time and simple algorithm. Finally, the testing process was carried out on test data using a deep learning benchmark model in accordance with the training process. The research results showed that a learning rate of 10-2 from the MobileNetV2 method produced an optimal performance with a training validation accuracy of 99.60% within a time of 312 min and 28 s.

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Section: B Data Preprocessingmentioning

confidence: 99%

Learning Rate Analysis for Pain Recognition Through Viola-Jones and Deep Learning Methods

Raihan Islamadina,

Khairun Saddami,

Fitri Arnia

et al. 2024

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…Hamad et al [18] also proposed a face recognition system that uses the Viola-Jones algorithm to detect facial areas and could achieve high accuracy. The Viola-Jones algorithm has also been used in multiple alternative applications [19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Alhammad,

Khafaga,

Hamed

et al. 2023

CSSE

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Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the realworld masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performance metrics for the proposed technique were measured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

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“…This technology uses algorithms and techniques for processing images to analyze pixels in an image or video frame and specify if there is a human face in it (Sinurat, 2022;Tasfia & Reno, 2022) . This analysis includes checking color, texture, and shape pixels to recognize patterns typical of humans' faces (Forsyth & Ponce, 2011) (Setia, 2021) (Mohibullah et al, 2022). Face detection has applications in various fields like system security and surveillance, devices for soft editing photos and videos, and social media platforms.…”

Section: Introductionmentioning

confidence: 99%

“…At the stage of feature extraction, algorithms analyze pictures or video frames to identify feature keys associated with human faces, like location of eyes, nose, and mouth. In stage classification, algorithms use these features to define There is a face present in images or video frames (Hirzi et al, 2021;Suprianto et al, 2013;Naveenkumar & Vadivel, 2018).…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness Of OpenCV Based Face Detection In Low-Light Environments

Julham,

Hutagalung,

Simalango

et al. 2023

JITE

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A face detection experiment based on OpenCV is a technology used to identify and find human faces in digital images or videos. This technology uses algorithms and image processing techniques to analyze the pixels in an image or video frame and determine if they contain a human face. The aim of this research is to determine the effectiveness of OpenCV-based face detection using the Viola-Jones algorithm with low-intensity brightness environmental conditions. The research was conducted with light intensity levels of 10 Lux, 30 Lux, and 50 Lux and was carried out using the camera on the ASUS TUF DASH 15 FX517ZC laptop. Data in evaluation using evaluation metrics containing the formula recall, precision, F-Score, and accuracy. The results of the study showed that experiments with higher light intensities up to 50 Lux showed the best level of efficiency according to the accuracy values (99.2%), f-score(0.996), and recall (0.993), so the system is best done with a brightness of 50 Lux. Face detection is affected by the camera that used, in addition, rotation of face is important for the system to detect faces, despite video recording in high light intensity environments

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Literature Study of Face Recognition using The Viola-Jones Algorithm

Cited by 10 publications

References 23 publications

Learning Rate Analysis for Pain Recognition Through Viola-Jones and Deep Learning Methods

Learning Rate Analysis for Pain Recognition Through Viola-Jones and Deep Learning Methods

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

The Effectiveness Of OpenCV Based Face Detection In Low-Light Environments

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