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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Rekayasa elektrika
Jane Litouw, Jane
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Low-resolution facial emotion recognition on low-cost devices Dwisnanto Putro, Muhamad; Litouw, Jane; Poekoel, Vecky Canisius
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2201-2211

Abstract

The low-resolution input image is a crucial challenge for applying facial emotion recognition in real-world scenarios. The critical problem is that valuable object features are relatively lost in the extraction process due to their small size. On the other hand, this vision system is required by a machine to run smoothly on lowcost devices. Facial emotion recognition using a lightweight feature extractor is proposed in this study to effectively capture crucial facial components in a lowresolution image. To compromise the running speed, this work offers an efficient feature convolution to discriminate specific facial features. In addition, the system is embedded with an attentive module to capture important features and correlate them. Our model performance is evaluated on low-resolution public datasets achieving the accuracy of 97.34%, 81.10%, and 80.12% on Karolinska directed emotional faces (KDEF), real-world affective faces database (RFDB), and facial expression recognition 2013 plus (FER2013Plus), respectively. The practical application demands that the deep learning model can operate fast on inexpensive devices. Consequently, the model achieved a speed of 290 frames per second (FPS) on a central processing unit (CPU) device.
Streamlining Deep Learning Network for Real-time Sea Turtle Detection Putro, Muhamad Dwisnanto; Mose, Yuliana; Andaria, Alex Copernikus; Litouw, Jane; Poekoel, Vecky Canisius; Najoan, Xaverius
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.35236

Abstract

Monitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. The need for increased model speed in the inference stage has led to many lightweight vision-based detectors. This work proposes a novel turtle detection to localize multiple sea turtles using a deep learning method. A lightweight primary extractor is applied to distinguish crucial features without producing a huge computational. An excited group attention is offered as an enhancement module that can capture essential turtle components in multi-level convolutional patches. A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. High-efficiency benefits models that can be implemented on low-end devices in terms of real-time data processing speed.
Co-Authors Andaria, Alex Copernikus Arie Lumenta Colia, Yeheskiel Dedi Sorongan, Yuliana Mose, Reonaldy A Berikang Dwisnanto Putro, Muhamad Mandagi, Marlon M. Mulya, Twi Ohoirat, Ferdinando G. Pinrolinvic D.K. Manembu Poekoel, Riecky S.S. Poekoel, Vecky Canisius Putro, Muhamad Dwisnanto REYNOLD F. ROBOT Robot, Reynold Sangian, Jurgen R. Vecky C. Poekoel Vecky Poekoel, Vecky Xaverius Najoan