Efficient and Effective Multi-person and Multi-angle Face Recognition based on Deep CNN Architecture

Tsai, An-Chao; Ou, Yang-Yen; Hsu, Liu-Yi-Cheng; Wang, Jhing-Fa

doi:10.1109/icot.2018.8705876

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The rapid development of computer hardware technology and software technology provides a new method to solve face recognition --a deep learning method based on a convolutional neural network. A convolutional neural network (CNN) uses a multi-layer neural network composed of basic mental elements to learn target features by simulating the learning way of the human brain [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-angle Face Recognition Based on GMRF

Sun

Zhao

Zhang

2021

Lecture Notes on Data Engineering and Communications Technologies

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The general face recognition methods mostly use positive face images or with a small angle of deflection. Such databases are well established. But in real life, face recognition problems often do not happen to be positive face state, especially in monitoring and public security monitoring. The existing effect of multi-angle face recognition is not ideal, and this paper proposes a face recognition method based on Gaussian Markov Random Fields (GMRF). GMRF model is a statistical probability model that can effectively extract image texture information, which first divides the face image into several sub-blocks in different chunks, and then, for each sub-block under the block mode, extracts the GMRF feature after wavelet transformation; finally combines the GMRF features of different blocking methods, and then classifies the SVM of the Gaussian nuclear function. Experiments were carried out on the self-built data set, and the proposed method reached 98.83% for face recognition.

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Section: Introductionmentioning

confidence: 99%

Multi-angle Face Recognition Based on GMRF

Sun

Zhao

Zhang

2021

Lecture Notes on Data Engineering and Communications Technologies

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“…As an example of user-authentication applications, always-on face detection/recognition is highly convenient because direct physical contact, such as fingerprint scanning, is unnecessary [2][3][4]. However, integrating always-on face detection/recognition into mobile devices is challenging because of these devices' limited battery life and thus limited power [5][6][7][8][9][10][11]. Therefore, a low-resolution and always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) that enables high power consuming devices, like ultra-high resolution (>tens of megapixels) CISs, to turn on for iris identification and face identification have received great attention [5].…”

Section: Introductionmentioning

confidence: 99%

“…The pixel data are transferred to an external CVP chip and stored in analog memory blocks before a complex deep convolutional neural network (CNN) operation in the CVP that allows the classification of large datasets [12]. In this case, transmitting the data to another chip that is unsuitable for low-power operation requires a great deal of power [8][9][10][11]. In order to enhance the power efficiency, either the parts of the CNN circuits or that of the CVP can be implemented with the CIS in a chip, as shown in Figure 1b.…”

Section: Introductionmentioning

confidence: 99%

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

Choi

Lee

Son

et al. 2020

Sensors

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This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 μm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 × 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120.

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Smart Advertising Based on Customer Preferences and Manage the Supermarket

Dinuka

et al. 2022

2022 4th International Conference on Advancements in Computing (ICAC)

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Efficient and Effective Multi-person and Multi-angle Face Recognition based on Deep CNN Architecture

Cited by 3 publications

References 13 publications

Multi-angle Face Recognition Based on GMRF

Multi-angle Face Recognition Based on GMRF

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

Smart Advertising Based on Customer Preferences and Manage the Supermarket

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