Deep learning inspired intelligent embedded system for haptic rendering of facial emotions to the blind

Saurav, Sumeet; Saini, Anil; Saini, Ramesh Kumar; Singh, Sanjay

doi:10.1007/s00521-021-06613-3

Cited by 11 publications

(3 citation statements)

References 80 publications

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“…In the process of speech signal processing, due to the influence of factors such as glottal excitation and oral and nasal radiation, the average power spectrum of the speech signal drops at a speed of 6 dB/oct at the high-frequency end. Therefore, when calculating the spectrum of the speech signal, the components at the high-frequency end are relatively small, which makes it difficult to obtain the spectrum of the high-frequency part ( Saurav et al, 2022 ). In response to the problems found above, it proposes a pre-emphasis technique to perform spectral analysis on channel parameters.…”

Section: English Flipped Classroom Teaching Mode Methods Based On Emo...mentioning

confidence: 99%

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Lin

2022

Front. Psychol.

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With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated teaching modes, not student-centered, etc., which lead to poor students’ enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher.

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Section: English Flipped Classroom Teaching Mode Methods Based On Emo...mentioning

confidence: 99%

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Lin

2022

Front. Psychol.

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“…In order to compare the performances of a small set of possible architectures, as demonstrated in methods, simulation results are obtained.The definition of efficient simulationbased methods focuses on reducing modelling complexity and increasing simulation speed. There exists a clear correlation between the accuracy and speed of simulation and the amount of abstraction used to model the system architecture [17,18]. Computational modelling and communication modelling are substantially separable and can be defined at different levels of abstraction on application and platform sides.…”

Section: Introductionmentioning

confidence: 99%

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

Jian Feng

2024

jes

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The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

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“…Facial expressions render crucial nonverbal cues and reveal an individual's mental state and intention. Therefore, besides playing primary roles in daily communication, a system for automatic facial expression recognition (FER) can play a major role in societal 1,2 and several other applications related to medical health analysis, 3,4 online teaching, 5 advanced driver assistance, 6,7 customer interest monitoring, 8 etc. But, these applications require robust and computationally efficient FER algorithms to ensure realisticity and enhance users' acceptability.…”

Section: Introductionmentioning

confidence: 99%

A dual‐channel ensembled deep convolutional neural network for facial expression recognition in the wild

Saurav

Saini

Singh

2023

Computational Intelligence

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Facial expression recognition (FER) in the wild is an active and challenging field of research. A system for automatic FER finds use in a wide range of applications related to advanced human–computer interaction (HCI), human–robot interaction (HRI), human behavioral analysis, gaming and entertainment, etc. Since their inception, convolutional neural networks (CNNs) have attained state‐of‐the‐art accuracy in the facial analysis task. However, recognizing facial expressions in the wild with high confidence running on a low‐cost embedded device remains challenging. To this end, this study presents an efficient dual‐channel ensembled deep CNN (DCE‐DCNN) for FER in the wild. Initially, two DCNNs, namely the and , are trained separately on the grayscale and Scharr‐convolved vertical gradient facial images, respectively. The proposed network later integrates the two pre‐trained DCNNs to obtain the dual‐channel integrated DCNN (DCI‐DCNN). Finally, all three neural networks, namely the , , and DCI‐DCNN, are jointly fine‐tuned to get a single dual‐channel‐multi‐output model. The multi‐output model produces three prediction scores for the given input facial image. The prediction scores are thus fused using the max‐voting ensemble scheme to obtain the DCE‐DCNN with the final classification label. On the FER2013, RAF‐DB, NCAER‐S, AffectNet, and CKPlus benchmark FER datasets, the proposed DCE‐DCNN consistently outperforms the two individual DCNNs and numerous state‐of‐the‐art CNNs. Moreover, the network achieves competitive recognition accuracy on all four FER in the wild datasets with reduced memory storage size and parameters. The proposed DCE‐DCNN model with high throughput on resource‐limited embedded devices is suitable for applications that seek real‐time classification of facial expressions in the wild with high confidence.

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Deep learning inspired intelligent embedded system for haptic rendering of facial emotions to the blind

Cited by 11 publications

References 80 publications

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

A dual‐channel ensembled deep convolutional neural network for facial expression recognition in the wild

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