A Review on Face Detection Based on Convolution Neural Network Techniques

Dhahir, Hayder Kadhim; Salman, Nassir H.

doi:10.24996/ijs.2022.63.4.39

Cited by 9 publications

(7 citation statements)

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“…Adaptive neural networks The ANN can detect complex patterns within the databases that the arithmetic formulas cannot find [8], [9], [10], and [11]. In addition, it produces quite accurate forecasts, even for noisy data.…”

Section: Artificial Neural Network (Anns) and Gismentioning

confidence: 99%

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Ali,

Mohammed,

Mohammed

2024

Iraqi Journal of Science

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A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network site. A network service area is a region that comprises all accessible roadways (that is, routes that are within defined impedance) (that is, streets that are within specified impedance). In contrast to the Google Maps application, the 5-minute service area for a site on a network comprises all streets that can be accessed within five minutes.

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Section: Artificial Neural Network (Anns) and Gismentioning

confidence: 99%

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Ali,

Mohammed,

Mohammed

2024

Iraqi Journal of Science

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“…Face detection happens after an image is read from the dataset [20], and its generation is performed using the deep MediaPipe technique. Note that MediaPipe face detection is a super-fast solution to detect face features, and it comes with six landmarks and multi-face support [21].…”

Section: Detect and Crop The Facementioning

confidence: 99%

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

2023

IJIES

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DeepFake is a concern for celebrities and everyone because it is simple to create. DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches. On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer. Moreover, the detection of video manipulation depends entirely on its detection through images. Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc. Also, the accuracy of their counterfeit detection in all previous studies was less than what this paper achieved, especially with the benchmark Flickr faces high-quality dataset (FFHQ). This study proposed, a new, simple, but powerful method called image Re-representation by combining the local binary pattern of multiple-channel (IR-CLBP-MC) color space as an image re-representation technique improved DeepFake detection accuracy. The IR-CLBP-MC is produced using the fundamental concept of the multiple-channel of the local binary pattern (MCLBP), an extension of the original LBP. The primary distinction is that in our method, the LBP decimal value is calculated in each local patch channel, merging them to re-represent the image and producing a new image with three color channels. A pretrained convolutional neural network (CNN) was utilized to extract the deep textural features from twelve sets of a dataset of IR-CLBP-MC images made from different color spaces: RGB, XYZ, HLS, HSV, YCbCr, and LAB. Other than that, the experimental results by applying the overlap and non-overlap techniques showed that the first technique was better with the IR-CLBP-MC, and the YCbCr image color space is the most accurate when used with the model and for both datasets. Extensive experimentation is done, and the high accuracy obtained are 99.4% in the FFHQ and 99.8% in the CelebFaces Attributes dataset (Celeb-A).

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“…Convolutional Neural Networks, often known as CNNs or ConvNets, are multi-stage deep architectures that combine convolutional layers with pooling or subsampling layers, followed by one or more fully connected layers. As stated by [10,21,22], its hierarchical network architecture makes it simpler to acquire invariant features and gather layer-by-layer In the illustration above, inputs are fed to develop a representation of the features using twophase convolutional and subsampling processes, and a Gaussian classifier is then used to produce a probabilistic distribution. The CNN is made up of three main components [14,23]: 1.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…The local connection, pooling operation, shared weight, and hierarchical design of the CNN are its four main features [22,24]. According to previous research on the function of the visual cortex, human cognition of the actual world extends from local to global.…”

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confidence: 99%

Transfer Learning Based Traffic Light Detection and Recognition Using CNN Inception-V3 Model

Kheder,

Mohammed

2023

Iraqi Journal of Science

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Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on a transfer learning-based model that uses the Inception-V3 network model to significantly reduce the amount of training data and computing costs. The proposed model was trained and tested in the laboratory for the intelligent and safe automobiles (LISA) traffic light dataset, which has been augmented by several pre-processing methods. Then, using different convolution and pooling techniques, the retrieved layer-wise features were compared and analyzed. Lastly, great reliability and repeatability are seen based on statistical analysis when the transfer learning-based model is frequently retrained utilizing precise tuning parameters. The results demonstrate that a transfer learning-based model is capable of high-level recognition performance in the proposed model, with an accuracy rate of 98.6%.

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A Review on Face Detection Based on Convolution Neural Network Techniques

Cited by 9 publications

References 0 publications

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

Transfer Learning Based Traffic Light Detection and Recognition Using CNN Inception-V3 Model

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