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Human-Robot Collaboration Using Sequential-Recurrent-Convolution-Network-Based Dynamic Face Emotion and Wireless Speech Command Recognitions
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DOI：10.1109/ACCESS.2022.3228825.png)
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摘要
En 中文
The proposed sequential recurrent convolution network (SRCN) includes two parts: one convolution neural network (CNN) and a sequence of long short-term memory (LSTM) models. The CNN is to achieve the feature vector of face emotion or speech command. Then, a sequence of LSTM models with the shared weight reflects a sequence of inputs provided by a (pre-trained) CNN with a sequence of input sub-images or spectrograms corresponding to face emotion and speech command, respectively. Simply put, one SRCN for dynamic face emotion recognition (SRCN-DFER) and another SRCN for wireless speech command recognition (SRCN-WSCR) are developed. The proposed approach not only effectively tackles the recognitions of dynamic mapping of face emotion and speech command with average generalized recognition rate of 98% and 96.7% but also prevents the overfitting problem in a noisy environment. The comparisons among mono and stereo visions, Deep CNN, and ResNet50 confirm the superiority of the proposed SRCN-DFER. The comparisons among SRCN-WSCR with noise-free data, SRCN-WSCR with noisy data, and multiclass support vector machine validate its robustness. Finally, the human-robot collaboration (HRC) using our developed omnidirectional service robot, including human and face detections, trajectory tracking by the previously designed adaptive stratified finite-time saturated control, face emotion and speech command recognitions, and music play, validates the effectiveness, feasibility, and robustness of the proposed method.
Keyword:
Face recognition
Speech recognition
Emotion recognition
Service robots
Human-robot interaction
Image recognition
Collaboration
Human--robot collaboration
CNN
LSTM
human and face detection
dynamic face emotion recognition
wireless speech command recognition
omnidirectional service robot
visual searching and tracking
adaptive stratified finite-time saturated control
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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