Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks

Riyantoko, Prismahardi Aji; Sugiarto, _; Hindrayani, Kartika Maulida

doi:10.1088/1742-6596/1844/1/012004

Cited by 12 publications

(9 citation statements)

References 2 publications

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“…Riyantoko et al 28 utilized the Haar-cascade classifier and CNN to classify face emotion and categorized seven facial expressions. Centered on epoch varieties, the CNN model gained MSE and the value of accuracy also increases.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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Comprehensive review and analysis on facial emotion recognition methods

Vats,

Kumar,

Rai

et al. 2023

J. Electron. Imag.

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Facial emotion recognition (FER) is a significant subject in computer vision and artificial intelligence because of its tremendous academic and commercial potential, such as in cognitive science, health care, virtual reality, and video conferencing in various domains. While FER could be carried out using multiple sensors, this review includes research that exclusively uses face images, since facial expressions are among the key pieces of knowledge in human interactions. We give a brief review of FER research carried out in recent years. We divided the techniques of FER mainly into two parts, i.e., based on the type of approach and based on the type of frame, which is further subdivided into two sub-parts of each classification for more detailed division. First, it explains traditional FER approaches together with a description of the representative classes of FER systems. Deep-learning FER strategies are then addressed using deep networks that allow "end-to-end" learning. This review is also directed toward an up-to-date deep learning strategy, which is a trending topic nowadays. A brief overview of publicly accessible assessment metrics is provided in the later part of this paper, as well as a comparison with the baseline results is presented, which is a norm for quantitative analysis of FER research. The whole analysis also could act as a concise field guide for beginners in the FER sector, providing general details and a common understanding of both the recent state-of-the-art research and established researchers searching for fruitful areas for further research.

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Section: Deep Learning Methodsmentioning

confidence: 99%

“…Riyantoko et al 28 . utilized the Haar-cascade classifier and CNN to classify face emotion and categorized seven facial expressions.…”

Section: Face Emotion Detection Techniquesmentioning

confidence: 99%

Comprehensive review and analysis on facial emotion recognition methods

Vats,

Kumar,

Rai

et al. 2023

J. Electron. Imag.

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“…For face recognition tasks, we employ the Face Encoding algorithm, which harnesses deep metric learning to create numerical encoding of facial features. These encoding are pivotal for discerning whether two faces are the same or different [20][21][22].…”

Section: ) Face Encoding (Dlib's Deep Metric Learning)mentioning

confidence: 99%

Enhancing a Real-time Face Recognition Accuracy With Innovative using Convolutional Neural Networks

Gamal,

Shayboub

2023

Preprint

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This paper introduces an innovative facial recognition criminal detection system that is at the forefront of technology. Its primary goal is to identify suspects in real-time, using cutting-edge algorithms and live camera technology. By combining these advanced features, the system significantly enhances security measures and helps combat criminal activities. Facial recognition has become a prominent tool in the field of security, revolutionizing surveillance for threat detection. The user-friendly desktop application offers a wide range of features, including the ability to view crime statistics and upload suspect images. With an impressive accuracy rate of 99.38%, the system excels at identifying potential criminals, thanks to its integration with live camera sensors. Administrators benefit from additional privileges, such as direct management of the suspect database, user account control, and system monitoring. An admin dashboard provides efficient oversight of suspects, approval requests, and user management, enabling effective decision-making.

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“…Penelitian deteksi wajah juga dilakukan oleh (Riyantoko et al, 2021) untuk membandingkan akurasi metode haar cascade classification dan metode convolutional neural networks (CNN) dalam melakukan klasifikasi emosi wajah. Hasil penelitian ini menemukan bahwa semakin tinggi nilai epoch maka nilai mean square error (MSE) semakin rendah.…”

Section: Studi Pustakaunclassified

Validation of the Haar Cascade Classification Method in Face Detection

Bahit,

Nadia Putri Utami,

Heru Kartika Candra

et al. 2023

JITE

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As technology develops, faces are used as a tool for human interaction with computers for security systems. Face detection technology can also provide convenience to users in various fields, especially security systems. However, there are problems regarding accuracy, complexity in the face recognition process so that many methods have been developed to increase the accuracy and complexity of the face detection process. This study aims to validate the haar cascade classification method in detecting faces from various shooting angles, with a distance of one meter from the camera and the respondent is free to make movements as well as various facial expressions and various lighting conditions that are different for each respondent. The results of this study found that the haar cascade classification method showed that the higher the epoch value, the lower the mean square error (MSE). This study also found that the haar cascade classification method has good accuracy for detecting faces from various angles, different lighting and different facial expressions with a maximum distance of one meter from the camera. This study provides recommendations for making face recognition applications using the haar cascade classification method because it can be used well for lighting effects, facial expressions and a maximum shooting distance of one meter.

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Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks

Cited by 12 publications

References 2 publications

Comprehensive review and analysis on facial emotion recognition methods

Comprehensive review and analysis on facial emotion recognition methods

Enhancing a Real-time Face Recognition Accuracy With Innovative using Convolutional Neural Networks

Validation of the Haar Cascade Classification Method in Face Detection

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