Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network

Madupu, Ram Kumar; Kothapalli, Chiranjeevi; Yarra, Vasanthi; Harika, S.; Basha, Cmak Zeelan

doi:10.1109/iceca49313.2020.9297483

Cited by 24 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study [3] focuses on addressing the challenges associated with Emotion Recognition Datasets and explores different parameters and architectures of Convolutional Neural Networks (CNNs) for the detection of seven emotions in human faces: anger, fear, disgust, contempt, happiness, sadness, and surprise. The proposed model achieves an accuracy of 91%, enabling effective tracking of human emotions through facial expressions [4]. High boost filtering is employed as a specialized technique to reduce image noise while preserving low-frequency components.…”

Section: Literature Reviewmentioning

confidence: 99%

D-Scan : Depression Detection

2024

IJSREM

View full text Add to dashboard Cite

Depression is a serious illness that affects millions of people globally. From child to senior citizen are facing depression. Major area is occupied by adults, college going students and teenagers also. In recent years, the task of automation depression detection from speech has gained popularity. We provide a comparative analyses of various features for depression detection by evaluating how a system built on text-based, voice-based, and speech-based system. Detecting texts that express negativity in the data is one of the best ways to detect depression. In this paper, this problem of depression detection on social media and various machine learning algorithms that can be used to detect depression have been discussed. Key Words: Depression, Face detection, Audio detection, Video detection, Healthcare innovation, Result.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

D-Scan : Depression Detection

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…The proposed model achieves an impressive accuracy of 91%, enabling the tracking of human emotions through facial expressions. [15]In the realm of social signal processing, emotion recognition from facial expressions plays a vital role in human-computer interaction. Although automatic emotion recognition using machine learning approaches has been extensively explored, accurately recognizing basic emotions like anger, happiness, disgust, fear, sadness, and surprise remains challenging in computer vision.…”

Section: Related Workmentioning

confidence: 99%

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

P V V S Srinivas

2023

IJRITCC

View full text Add to dashboard Cite

In facial emotion detection, researchers are actively exploring effective methods to identify and understand facial expressions. This study introduces a novel mechanism for emotion identification using diverse facial photos captured under varying lighting conditions. A meticulously pre-processed dataset ensures data consistency and quality. Leveraging deep learning architectures, the study utilizes feature extraction techniques to capture subtle emotive cues and build an emotion classification model using convolutional neural networks (CNNs). The proposed methodology achieves an impressive 97% accuracy on the validation set, outperforming previous methods in terms of accuracy and robustness. Challenges such as lighting variations, head posture, and occlusions are acknowledged, and multimodal approaches incorporating additional modalities like auditory or physiological data are suggested for further improvement. The outcomes of this research have wide-ranging implications for affective computing, human-computer interaction, and mental health diagnosis, advancing the field of facial emotion identification and paving the way for sophisticated technology capable of understanding and responding to human emotions across diverse domains.

show abstract

“…The SURF was used features extraction. The experiments were conducted on own local dataset of 200 individual people's images of faces (7) . The BPNN and CNN are used to evaluate the facial model.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition

Harakannanavar¹,

Sapnakumari²,

Ramachandra³

et al. 2023

IJST

View full text Add to dashboard Cite

Objectives: To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models. Methods: This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques form a feature vector. Then, the fusion of all the features is carried out at the feature level. The multiclass SVM and KNN classifiers are used to recognize the facial expressions, effectively. Findings: In this work, an effort is made to develop a robust FER model using JAFFE database. It is recorded that, based on the experimental results, the proposed model suits better with a performance rate of 98.26% for SVM and 96.51% for KNN, when compared with the different state-of-the-art methods. Novelty: Many FER models have been developed and adopted for enhancing their quality and to extract the facial features using transform and frequency domains. It is observed that, maximum approaches are based on generating the texture features. The fusion at the feature level using modified HoG, LBP and FKBD is performed and the SVM model is more compatible when compared with other classifiers and it supports one-to-one and one-to-many comparisons' technique.

show abstract

Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network

Cited by 24 publications

References 11 publications

D-Scan : Depression Detection

D-Scan : Depression Detection

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition

Contact Info

Product

Resources

About