Development of Artificial Neural Network Architecture for Face Recognition in Real Time

Supardi, Julian; Utami, Alvi Syahrini

doi:10.7763/ijmlc.2014.v4.396

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…The calculation of a fully connected layer is using the backpropagation algorithm [7]. Let 𝑉 be the weights between FC-1 and FC-2 layers, and 𝑊 be the weights between FC-2 and FC-3 layers.…”

Section: Fully Connected Layermentioning

confidence: 99%

“…Besides, facial recognition systems have been successfully implemented in many applications in the real world, such as at immigration check-in counter, security systems, and attendance recording machines. Moreover, some methods of face recognition systems have been implemented successfully, such as Principle Component Analysis (PCA) [1][2] [3], Linear Discriminant Analysis (LDA) [4], Singular Value Decomposition (SVD) [5], Canonical Correlation Analysis (CCA) [6], Artificial Neural Network (ANN) [7], and Deep Learning [8]. However, a surveillance camera usually produces a small size of face image as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Recognizing Very Small Face Images Using Convolution Neural Networks

Horng

Supardi

Zhou

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Face recognition can be installed in a surveillance system so that it can be used for monitoring, tracking and access control. An excellent, intelligent surveillance system should be sensitive to the objects far away from the camera. Unfortunately, due to the long-distance, objects like human faces captured by the camera are too small to identify. As to enhance the subtle color differences in the face image, in this paper though we first improve the resolution of the captured image using deep convolution neural networks (DCNNs). Then the efficient features are extracted and used to do classification. As for verifying the effectiveness of the proposed method, we used three databases including AR face database, Georgia Tech face database (GT) database, and Labelled Faces in the Wild (LFW) database, altogether, to conduct the training and testing. Compared to the existing approaches, experimental results show that the identification accuracy of the proposed method outperforms to any existing approaches.

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Section: Fully Connected Layermentioning

confidence: 99%