Emotional Recognition System by Facial Land Marking Analysis

2018 ◽  
Author(s):  
Akhil Tolani ◽  
Nirbhay Kashyap ◽  
Tanupriya Choudhury ◽  
Shaswat Shukla
Keyword(s):  
Recognition System ◽  
Emotional Recognition

scholarly journals Audio-Textual Emotion Recognition Based on Improved Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Linqin Cai ◽  
Yaxin Hu ◽  
Jiangong Dong ◽  
Sitong Zhou
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Short Term ◽  
Term Memory ◽  
Emotional Recognition ◽  
Long Short Term Memory

With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

scholarly journals New emotional model environment for navigation in a virtual reality

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 864-870
Author(s):  
Marcin Daszuta ◽  
Dominik Szajerman ◽  
Piotr Napieralski
Keyword(s):  
Virtual Reality ◽  
Emotional Experience ◽  
Recognition System ◽  
Emotional Recognition ◽  
Emotional Model ◽  
Desirable Feature ◽  
Human Emotions ◽  
Model Environment

Abstract Emotions are commonly considered to be the most expressive of everyday human experiences, giving a sense of fullness and reality of life. The ability to recognize human emotions as a manifestation of higher intelligence is desirable feature. There are several models of emotional experience that can become the basis for building a universal emotional recognition system. In this article, we check the correctness of the designed emotional model. We check the evaluation of the system’s operation by human observers.

scholarly journals Implementation of FPGA accelerator architecture for Convolution Neural Network in Emotional Recognition System

2021 ◽  
Vol 12 (6) ◽  
pp. 127-135
Author(s):  
Samson Immanuel J Et.al
Keyword(s):  
Integrated Circuits ◽  
Field Programmable Gate Array ◽  
Recognition System ◽  
Memory Transfer ◽  
Emotional Recognition ◽  
Application Specific Integrated Circuits ◽  
Field Programmable ◽  
Real Time Applications ◽  
Application Specific ◽  
The Ideal

The field of deep learning, artificial intelligence has arisen due to the later advancements in computerized innovation and the accessibility of data information, has exhibited its ability and adequacy in taking care of complex issues in learning that were not previously conceivable. The viability in emotional detection and acknowledging specific applications have demonstrated by Convolution neural networks (CNNs). In any case, concentrated Processor activities and memory transfer speed are necessitated that cause general CPUs to neglect to accomplish the ideal degrees of execution. Subsequently, to build the throughput of CNNs, equipment quickening agents utilizing General Processing Units (GPUs), Field Programmable Gate Array (FPGAs) and Application Specific Integrated circuits (ASICs) has been used. We feature the primary highlights utilized for productivity improving by various techniques for speeding up. Likewise, we offer rules to upgrade the utilization of FPGAs for the speeding up of CNNs. The proposed algorithm on to an FPGA platform and show that emotions recognition utterance duration 1.5s is identified in 1.75ms, while utilizing 75% of the resources. This further demonstrates the suitability of our approach for real-time applications on Emotional Recognition system.

scholarly journals Emotional Recognition System Using Eigenfaces

2003 ◽  
Vol 13 (2) ◽  
pp. 216-221 ◽  
Author(s):  
Young-Hoon Joo ◽  
Sang-Yun Lee ◽  
Kwee-Bo Sim
Keyword(s):  
Recognition System ◽  
Emotional Recognition

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

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