Emotion, Age and Gender Recognition using SURF, BRISK, M-SVM and Modified CNN

Sikder, Juel; Datta, Nippon; Tripura, Sajib; Das, Utpol Kanti

doi:10.1109/icecet55527.2022.9872771

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…3) Define modified CNN model: CNN which is known as convolutional neural network is a widely used approach in deep learning to analyze visual imagery. Convolutional neural networks are used for image classification, object detection, segmentation and a variety of other applications [13], [14]. CNN architecture is divided into two components.…”

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confidence: 99%

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Nippon

Juel

2022

Preprint

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At the same time, recognizing the emotions, gender and age of a person is a vital issue in real life. This research focuses on identifying the emotions along with age and gender of different ages male, female, common gender and children. In this study, a modified convolutional neural network (M-CNN) has been proposed for feature extraction where classification is followed by a Multi Support Vector Machine (M-SVM). Instead of detecting the face, a head-detecting technique is also used in the prediction part. To detect the head we proposed a system using the You Only Look Once Version 3 (YOLOV3) network. It helps to extract the feature from the whole head instead of the only face. Three datasets named AR-2022, FER-2022 and GR-2022 are introduced in this study. We also include the common gender in our datasets. In the case of age classification, we categorized the age datasets into 11 categories depending on the similarity of their features, gender into 3 categories and emotions into 7 categories. Our proposed model is compared with other existing models and current research, where the proposed model gives better accuracy and the accuracy of classification of emotions, age and gender are 97.47%, 98.43% and 96.65% respectively.

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Section: ) Load Datasets and Image Categoriesmentioning

confidence: 99%

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Nippon

Juel

2022

Preprint

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“…3) Define CNN-BiLSTM Model: CNN stands for the convolutional neural network is a deep learning-based approach used to extract the features from input data and learn from those extracted features. It is most useful in finding patterns in test images that assist to recognize objects, faces, emotions etc [12], [13]. On the other hand, Bi-LSTM stands for Bidirectional LSTM.…”

Section: Before Morphologicalmentioning

confidence: 99%

A Deep Learning Approach for Recognizing Bengali Characters and Numbers

Sikder

Datta

Das

2022

Preprint

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Bengali is one of the world's most widely spoken languages. But it is a challenging issue to recognize Bengali Alphabet and Numbers from handwritten documents. It has 50 fundamental alphabets and 10 Fundamental Numbers. Because of their diverse sizes, similarities between alphabets, and distinct writing methods, recognizing the Bengali handwritten Alphabet and Numbers is a difficult task. In this research, a deep learning-based approach is proposed combining CNN-BiLSTM for recognizing Bengali Alphabets as well as Numbers and the classification is followed by a multi-support vector machine (M-SVM). We introduced a dataset named Bengali Handwritten Alphabet and Numbers (BHAN-2022) dataset which was applied to evaluate the perfection of the proposed system. A Morphological Operation is also used before testing the input image which assists the model to remove the unnecessary part from the input image. The experimental findings show that the suggested model recognizes Bengali handwritten Alphabets as well as Numbers with an average accuracy of 97.08%.

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AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education during the Post-COVID-19 Era

Mehrabi,

Morphew,

Araabi

et al. 2024

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The onset of the COVID-19 pandemic has compelled a swift transformation in higher-education methodologies, particularly in the domain of course modality. This study highlights the potential for artificial intelligence and machine learning to improve decision-making in advanced engineering education. We focus on the potential for large existing datasets to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and e-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees, support vector machines, and random forest—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID-19 educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.

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Emotion, Age and Gender Recognition using SURF, BRISK, M-SVM and Modified CNN

Cited by 3 publications

References 14 publications

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

A Deep Learning Approach for Recognizing Bengali Characters and Numbers

AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education during the Post-COVID-19 Era

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