Facial Expression Detection Model of Seven Expression Types Using Hybrid Feature Selection and Deep CNN

Srinivas, P V V S; Mishra, Pragnyaban

doi:10.1007/978-981-33-6176-8_10

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Happy, Angry, Sad, Surprise, Neutral, Disgust, and Fear are the seven basic face emotion classes that the model learns during learning stage. Recently [7] [8], researchers have achieved remarkable progress in facial expression identification with higher number of classes [9], leading to advancements in neurology and applied mathematics, etc that are boosting research in the field of facial expression further. Moreover, advances in computer vision and machine learning have made emotion recognition more accurate tools of classification.…”

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confidence: 99%

An Improvised Facial Emotion Recognition System using the Optimized Convolutional Neural Network Model with Dropout

Srinivas¹,

Mishra²

2021

IJACSA

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Facial expression detection has long been regarded as both verbal and nonverbal communication. The muscular expression on a person's face reflects their physical and mental state. Using computer programming to integrate all face curves into a categorization class is significantly more important than doing so manually. Convolutional Neural Networks, an Artificial Intelligence approach, was recently developed to improve the task with more acceptance. Due to overfit during the learning step, the model performance may be lowered and regarded underperforming. There is a method dropout uses to reduce testing error. The influence of dropout is applied at convolutional layers and dense layers to classify face emotions into a distinct category of Happy, Angry, Sad, Surprise, Neutral, Disgust, and Fear and is represented as an improved convolutional neural network model. The experimental setup used the datasets namely JAFEE, CK48, FER2013, RVDSR, CREMAD and a selfprepared dataset of 36,153 facial images for observing train and test accuracy in presence and absence of dropout. Test accuracies of 92.33, 96.50, 97.78, 99.44, and 98.68 are obtained on Fer2013, RVDSR, CREMA-D, CK48, and JAFFE datasets are obtained in presence of dropout. The used features are countably large in the computation as a result the higher computation support of NVDIA with the capacity of GPU 16GB, CPU 13GB and memory 73.1 GB are used for the experimental purposes.

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confidence: 99%

An Improvised Facial Emotion Recognition System using the Optimized Convolutional Neural Network Model with Dropout

Srinivas¹,

Mishra²

2021

IJACSA

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“…About 93% of communication with humans is done through nonverbal means such as voice tone, facial expressions, and body language [7]. Identifying emotions through facial expressions which has been extensively studied [8] [9] resulted in higher accuracies by making the changes at the pre-processing stage. To reduce overfitting during the training stage, adding dropout to the CNN model plays a prominent role in reducing overfitting during training [10].…”

Section: Introductionmentioning

confidence: 99%

Human Emotion Recognition by Integrating Facial and Speech Features: An Implementation of Multimodal Framework using CNN

Srinivas¹,

Mishra²

2022

IJACSA

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This Emotion recognition plays a prominent role in today's intelligent system applications. Human computer interface, health care, law, and entertainment are a few of the applications where emotion recognition is used. Humans convey their emotions in the form of text, voice, and facial expressions, thus developing a multimodal emotional recognition system playing a crucial role in human-computer or intelligent system communication. The majority of established emotional recognition algorithms only identify emotions in unique data, such as text, audio, or image data. A multimodal system uses information from a variety of sources and fuses the information by using fusion techniques and categories to improve recognition accuracy. In this paper, a multimodal system to recognise emotions was presented that fuses the features from information obtained from heterogenous modalities like audio and video. For audio feature extraction energy, zero crossing rate and Mel-Frequency Cepstral Coefficients (MFCC) techniques are considered. Of these, MFCC produced promising results. For video feature extraction, first the videos are converted to frames and stored in a linear scale space by using a spatial temporal Gaussian Kernel. The features from the images are further extracted by applying a Gaussian weighted function to the second momentum matrix of linear scale space data. The Marginal Fisher Analysis (MFA) fusion method is used to fuse both the audio and video features, and the resulted features are given to the FERCNN model for evaluation. For experimentation, the RAVDESS and CREMAD datasets, which contain audio and video data, are used. Accuracy levels of 95.56, 96.28, and 95.07 on the RAVDESS dataset and accuracies of 80.50, 97.88, and 69.66 on the CREMAD dataset in audio, video, and multimodal modalities are achieved, whose performance is better than the existing multimodal systems.

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“…We have adopted a deep CNN in our research. The input to the architecture is preprocessed facial image which is filtered by various filters [20] as a result the quality of the image is enhanced. Filters have different measures for smoothing the image by removing impulse noise as per the function it uses.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

Mishra¹,

Srinivas²

2021

IJ-AI

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<p><span lang="EN-US">The facial emotion recognition by the machine is a challenging task. From decades, researchers applied different methods to classify facial emotion into the different classes. The expansion of artificial intelligence in a form of deep convolutional neural network (CNN) changed the direction of the research. The facial emotion recognition using deep CNN is powerful in terms of taking bulk input images for processing and classify with high accuracy. It has been noticed in a few cases the classification model does not judge the facial images into appropriate classes due to the influence of noises. So, it is highly recommended to apply a noiseless image to the facial emotion recognition model for classification. We adopted a mechanism and proposed a model for classifying facial image into one of the seven classes with high accuracy. The images are smoothed before applying to the model by different smoothing process as part of image preprocessing. We claim facial emotion recognition with image smoothing by different filters or a mixture of filter are more robust than without preprocessing. The detail is explained in the subsequent sections.</span></p>

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Facial Expression Detection Model of Seven Expression Types Using Hybrid Feature Selection and Deep CNN

Cited by 5 publications

References 20 publications

An Improvised Facial Emotion Recognition System using the Optimized Convolutional Neural Network Model with Dropout

An Improvised Facial Emotion Recognition System using the Optimized Convolutional Neural Network Model with Dropout

Human Emotion Recognition by Integrating Facial and Speech Features: An Implementation of Multimodal Framework using CNN

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

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