Facial Expressions Recognition System Using FPGA-Based Convolutional Neural Network

AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽

10.3390/s21072540 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2540

Author(s):

Zhipeng Yu ◽

Jianghai Zhao ◽

Yucheng Wang ◽

Linglong He ◽

Shaonan Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Gesture Recognition ◽

Recognition System ◽

Surface Emg ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Training Time ◽

Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

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Number Plate Recognition System Using Convolutional Neural Network

10.1109/icrito51393.2021.9596134 ◽

2021 ◽

Author(s):

Preeti Arora ◽

Vinod M Kapse ◽

Sapna Sinha ◽

Saksham Gera

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recognition System

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Learning Facial Expressions with 3D Mesh Convolutional Neural Network

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3200572 ◽

2019 ◽

Vol 10 (1) ◽

pp. 1-22 ◽

Cited By ~ 3

Author(s):

Hai Jin ◽

Yuanfeng Lian ◽

Jing Hua

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Facial Expressions ◽

3D Mesh

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Social Touch Gesture Recognition Using Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2018/6973103 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Saad Albawi ◽

Oguz Bayat ◽

Saad Al-Azawi ◽

Osman N. Ucan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gesture Recognition ◽

Classification Accuracy ◽

Recognition System ◽

Human Robot Interaction ◽

Sensor Data ◽

Social Touch ◽

Minimum Number ◽

Original Frame

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

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Deep learning based facial expressions recognition system for assisting visually impaired persons

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i3.2030 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1208-1219

Author(s):

Hendra Kusuma ◽

Muhammad Attamimi ◽

Hasby Fahrudin

Keyword(s):

Deep Learning ◽

Facial Expressions ◽

Visually Impaired ◽

Learning Algorithm ◽

Recognition System ◽

Verbal Information ◽

Deep Learning Algorithm ◽

Facial Recognition System ◽

Visually Impaired Persons ◽

Facial Expressions Recognition

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

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