Global Facial Recognition Using Gabor Wavelet, Support Vector Machines and 3D Face Models

Moreano, José Augusto Cadena; Palomino, Nora Bertha La Serna

doi:10.12720/jait.11.3.143-148

Cited by 16 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, as shown in Table 3, FaceNet plays a role in the feature extraction stage, where additional features are extracted and normalized. The input for this extraction method was 3 channel (RGB) photos, and the output was 128-dimensional vectors [31]. With its 22 layers, FaceNet is able to accurately and efficiently extract features from facial images, and its output/features may be trained into 128-dimensional embeddings [32].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Facial recognition for partially occluded faces

Naser

Ahmad

Samsudin

et al. 2023

IJEECS

View full text Add to dashboard Cite

Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Facial recognition for partially occluded faces

Naser

Ahmad

Samsudin

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…By using wavelet Gabor filter and SVM, the authors in [27] have successfully built a 3D facial recognition system. The highest accuracy that the proposed system achieved was 97.3%.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition System Design and Implementation using Neural Networks

Alsyayadeh,

Aziz

et al. 2022

IJACSA

View full text Add to dashboard Cite

Face recognition technology is used in biometric security systems to identify a person digitally before granting the access to the system or the data in it. There are many kidnappings or abduction cases happen around us, however, the kidnap suspects will be set free if there is lack of evidence or when the victims are not able to testify in court because they suffer from post-traumatic stress disorder (PTSD). The objectives of this study are, to develop a device that will capture the image of a kidnapper as evidence for future reference and send the captured image to the family of the victim through email, to design a face recognition system to be used in searching kidnap suspects and to determine the best training parameters for the convolution neural network (CNN) layers used by the proposed face recognition system. The accuracy of the proposed system is tested with three different datasets, namely the AT&T database, face database from [23] and a custom face dataset. The results are 87.50%, 92.19% and 95.93% respectively. The overall face recognition accuracy of the proposed system is 98.48%. The best training parameters for the proposed CNN model are kernel size of 5x5, 32 and 64 filters for first and second convolutional layers and learning rate of 0.001.

show abstract

“…It has two major advantages as follows: the ability to generate nonlinear decision with linear classifiers and the ability of the kernel to allow classifiers applied to a no obvious dimensional vector space representation data. In modelling SVM a hyperplane is constructed to form decision boundary creating margin between positive and negative class [30,31].…”

Section: Support Vector Machinementioning

confidence: 99%

“…The decision boundary of the classifier is the boundary region classified as positive or negative. A classifier that has a linear decision boundary is referred to as a linear classifier otherwise if dependent on non-linear data is known as non-linear classifier [31,32] The type of kernel function, the degree of kernel function (d: polynomial kernel; γ: for the radial base kernel) and regularisation constant c forms the parameter of an SVM. To determine efficient parameters we used the approach proposed in [34].…”

Section: Support Vector Machinementioning

confidence: 99%

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Bisandu

Homaid

Moulitsas

et al. 2021

2021 the 5th International Conference on Advances in Artificial Intelligence (ICAAI)

View full text Add to dashboard Cite

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in the process of decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for aviation decision system of the future cannot be over-emphasized. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector Machine models on a binary classification problem. The research revealed that different accuracies of flight delay classifications were achieved by the models. The Support Vector Machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector Machines and Neural Network with best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model unlike when hidden layers are increased, it does not ensure better or higher accuracy in a binary classification of flight delays. Finally, we recommended further studies on the applicability of Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the performance of the model.

show abstract

Global Facial Recognition Using Gabor Wavelet, Support Vector Machines and 3D Face Models

Cited by 16 publications

References 12 publications

Facial recognition for partially occluded faces

Facial recognition for partially occluded faces

Face Recognition System Design and Implementation using Neural Networks

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Contact Info

Product

Resources

About