ArticleSensors (Basel, Switzerland)2020
Facial Expressions Recognition for Human-Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Emotion Detection with Computer Vision and Deep Learning.Sensors (Basel, Switzerland) · 2024Pooled it
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- An improved facial emotion recognition system using convolutional neural network for the optimization of human robot interaction.Scientific reports · 2025Article
- An interactive information based DCNN-BiLSTM model with dual attention mechanism for facial expression recognition.Scientific reports · 2025Article
- OSC-COFrontiers in plant science · 2023Article
- Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.Sensors (Basel, Switzerland) · 2022Article
- Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays.Journal of clinical medicine · 2022Article
- The Hybrid Position/Force Walking Robot Control Using Extenics Theory and Neutrosophic Logic Decision.Sensors (Basel, Switzerland) · 2022Article
- Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In Management.Computational intelligence and neuroscience · 2022Article
- Improving Human-Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression.Sensors (Basel, Switzerland) · 2021Article
- The Analysis of Emotion Authenticity Based on Facial Micromovements.Sensors (Basel, Switzerland) · 2021Article
- Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine.Journal of personalized medicine · 2021Article
- Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots.Frontiers in neurorobotics · 2021Article
- Advanced Intelligent Control through Versatile Intelligent Portable Platforms.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression recognition (FER) in order to obtain real-time inference speed for the entire process. Two different models for FR are considered, one known to be very accurate, but has low inference speed (faster region-based convolutional neural network), and one that is not as accurate but has high inference speed (single shot detector convolutional neural network). For emotion recognition transfer learning and fine-tuning of three CNN models (VGG, Inception V3 and ResNet) has been used. The overall results show that single shot detector convolutional neural network (SSD CNN) and faster region-based convolutional neural network (Faster R-CNN) models for face detection share almost the same accuracy: 97.8% for Faster R-CNN on PASCAL visual object classes (PASCAL VOCs) evaluation metrics and 97.42% for SSD Inception. In terms of FER, ResNet obtained the highest training accuracy (90.14%), while the visual geometry group (VGG) network had 87% accuracy and Inception V3 reached 81%. The results show improvements over 10% when using two serialized CNN, instead of using only the FER CNN, while the recent optimization model, called rectified adaptive moment optimization (RAdam), lead to a better generalization and accuracy improvement of 3%-4% on each emotion recognition CNN.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.