Neural Network-based Face Pose Tracking for Interactive Face Recognition System

Achmad, Balza; Firdausy, Kartika

doi:10.18517/ijaseit.2.1.164

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…This VGG-19 architecture uses an alternating structure of several non-linear activation layers, uses max-pooling for downsampling, and modifies the Rectified Linear Unit (ReLU) as an activation function choosing the largest value in the area of an image. The down-sampling layer is used to increase the anti-distortion capability of an image, maintain the sample's main features, and reduce the number of parameters [32]- [34].…”

Section: Architecture Visual Geometry Group (Vgg)-19mentioning

confidence: 99%

Performance of ShuffleNet and VGG-19 Architectural Classification Models for Face Recognition in Autistic Children

Melinda¹,

Oktiana²,

Nurdin³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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This study discusses the face recognition of children with special needs, especially those with autism. Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects social skills, ways of interacting, and communication disorders. Facial recognition in autistic children is needed to help detect autism quickly to minimize the risk of further complications. There is extraordinarily little research on facial recognition of autistic children, and the resulting system is not fully accurate. This research proposes using the Convolution Neural Network (CNN) model using two architectures: ShuffleNet, which uses randomization channels, and Visual Geometry Group (VGG)-19, which has 19 layers for the classification process. The research object used in the face recognition system is secondary data obtained through the Kaggle site with a total of 2,940 image data consisting of images of autism and non-autism. The faces of autistic children are visually difficult to distinguish from those of normal children. Therefore, this system was built to recognize the faces of people with autism. The method used in this research is applying the CNN model to autism face recognition through images by comparing two architectures to see their best performance. Autism and non-autism data are grouped into training data, 2,540, and test data, as much as 300. In the training stage, the data was validated using validation data consisting of 50 autism image data and 50 non-autism image data. The experimental results show that the VGG-19 has high accuracy at 98%, while ShuffleNet is 88%.

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Section: Architecture Visual Geometry Group (Vgg)-19mentioning

confidence: 99%

Performance of ShuffleNet and VGG-19 Architectural Classification Models for Face Recognition in Autistic Children

Melinda¹,

Oktiana²,

Nurdin³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The problem of tuning OBFNs is in many aspects similar to training a deep neural network. It has been proven that neural network has been tremendously used to solve wide range of problems, such as in biomedics [15], fault detection system [16], power systems [17], face recognition [18], and telecommunication systems [19]. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Tuning Optical Beamforming Networks

et al. 2018

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In communication between planes and satellites, Optical Beamforming Networks (OBFNs), which rely on many small and flat Phased Array Antennas (PAAs), need to be tuned in order to receive signals from specific angles. In this paper, we develop a deep neural network representation of tuning OBFNs. The problem of tuning an OBFN is in many aspects similar to training a deep neural network. We present a way to exploit the special structure of OBFNs into deep neural network and an algorithm for tuning OBFNs based on feedback that can be easily measured in real system. Training data, which consists of full signals, can be measured, and therefore is used in this paper. For pilot signals, the desired signal is known explicitly. Given the configuration of OBFNs and all nominal parameters required, it was verified in simulation that the deep neural network can be used to tune large scale OBFNs for any desired delays.

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