Facial Emotion Detection Using Deep Learning

Jaiswal, Akriti; Raju, A. Krishnama; Deb, Suman

doi:10.1109/incet49848.2020.9154121

Cited by 114 publications

(36 citation statements)

References 9 publications

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“…Jaiswal et al [14] proposed a deep learning architecture of two different CNN networks for facial emotion detection. The proposed model uses Keras and has an input shape of 48*48*1 and two models with the same kernel size for feature extraction.…”

Section: Dataset For Facial Emotion Detection Systemsmentioning

confidence: 99%

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

Baffour

Nunoo‐Mensah

Keelson

et al. 2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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Facial emotion recognition (FER) forms part of affective computing, where computers are trained to recognize human emotion from human expressions. Facial Emotion Recognition is very necessary for bridging the communication gap between humans and computers because facial expressions are a form of communication that transmits 55% of a person's emotional and mental state in a total face-to-face communication spectrum. Breakthroughs in this field also make computer systems (robotic systems) better serve or interact with humans. Research has far advanced for this cause, and Deep learning is at its heart. This paper systematically discusses state-of-the-art deep learning architectures and algorithms for facial emotion detection and recognition. The paper also reveals the dominance of CNN architectures over other known architectures like RNNs and SVMs, highlighting the contributions, model performance, and limitations of the reviewed state-of-the-art. It further identifies available opportunities and open issues worth considering by various FER research in the future. This paper will also discover how computation power and availability of large facial emotion datasets have also limited the pace of progress.

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Section: Dataset For Facial Emotion Detection Systemsmentioning

confidence: 99%

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

Baffour

Nunoo‐Mensah

Keelson

et al. 2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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“…Penelitian oleh [3] mengusulkan Tsception yaitu sebuah kerangka Deep Learning yang digunakan untuk mendeteksi emosi dari electroencephalogram (EEG) untuk dibandingkan dengan hasil dari model SVM, EEGNET dan LSTM. CNN sebagai salah satu metode Deep Learning juga banyak diaplikasikan untuk mendeteksi emosi seperti penelitian yang dilakukan oleh [4] dan [5].…”

Section: Pendahuluanunclassified

Deteksi Emosi Berdasarkan Wicara Menggunakan Deep Learning Model

Rahmadani

Rahayu

Salim

et al. 2022

JINTEKS

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Kemampuan komputer dalam meniru kemapuan manusia sampai saat ini menjadi hal yang menarik untuk dikembangkan. Di beberapa studi, pengenalan emosi sudah banyak diteliti baik melalui foto wajah dan ucapan verbal maupun non verbal. Penelitian ini bertujuan untuk mengeksplorasi berbagai metode Deep Learning untuk mendapatkan model yang paling baik dalam mendeteksi emosi menggunakan dataset EmoDB. Feature extraction dilakukan dengan menggunakan Zero Crossing Rate, Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), Root Mean Square (RMS) dan MelSpectogram. Pada tahap preprocessing diterapkan teknik data augmentation dengan mengaplikasikan noise injection, shifting time dan mengubah pitch dan kecepatan audio. Dari hasil penelitian dikemukakan bahwa metode deep learning terbaik berdasarkan nilai akurasi adalah CNN-BiLSTM.

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“…It helps to capture both logical and emotional information. Commonly, this technique includes various movements of the cheek, chin, eyes, wrinkles and mouth [ 46 , 47 , 48 ].…”

Section: Sensors For Human Emotion Recognitionmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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Facial Emotion Detection Using Deep Learning

Cited by 114 publications

References 9 publications

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

Deteksi Emosi Berdasarkan Wicara Menggunakan Deep Learning Model

Development and Progress in Sensors and Technologies for Human Emotion Recognition

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