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A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

Institute of Computer Science, Pedagogical University of Krakow, 2 Podchorazych Ave, 30-084 Krakow, Poland
Sensors 2020, 20(7), 2140; https://doi.org/10.3390/s20072140
Received: 9 March 2020 / Revised: 1 April 2020 / Accepted: 8 April 2020 / Published: 10 April 2020
(This article belongs to the Section State-of-the-Art Sensors Technologies)
This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research. View Full-Text
Keywords: facial image annotation; RGB camera sensor; neural network; deep neural network; eigenfaces; nearest neighbour classifier; low power consumption computer facial image annotation; RGB camera sensor; neural network; deep neural network; eigenfaces; nearest neighbour classifier; low power consumption computer
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MDPI and ACS Style

Hachaj, T. A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices. Sensors 2020, 20, 2140. https://doi.org/10.3390/s20072140

AMA Style

Hachaj T. A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices. Sensors. 2020; 20(7):2140. https://doi.org/10.3390/s20072140

Chicago/Turabian Style

Hachaj, Tomasz. 2020. "A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices" Sensors 20, no. 7: 2140. https://doi.org/10.3390/s20072140

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