Race Classification from Face using Deep Convolutional Neural Networks

Wu, Xulei; Yuan, Pingzhi; Wang, Tianmiao; Gao, Doudou; Cai, Ying

doi:10.1109/icarm.2018.8610704

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…The statements of Rizvi et al [64] are also confirmed by Wu et al [65] who state that race (ethnicity) should be included among the basic and key attributes of facial analysis. The issue with automatic face analysis is that traditional machine learning methods deal with race classification only in combination with two separate steps: extracting artificially designed features and training the right classifier with these features.…”

Section: Related Workmentioning

confidence: 79%

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A Case Study of Facial Emotion Classification Using Affdex

Magdin

Benko

Koprda

2019

Sensors

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This paper focuses on the analysis of reactions captured by the face analysis system. The experiment was conducted on a sample of 50 university students. Each student was shown 100 random images and the student´s reaction to every image was recorded. The recorded reactions were subsequently compared to the reaction of the image that was expected. The results of the experiment have shown several imperfections of the face analysis system. The system has difficulties classifying expressions and cannot detect and identify inner emotions that a person may experience when shown the image. Face analysis systems can only detect emotions that are expressed externally on a face by physiological changes in certain parts of the face.

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Section: Related Workmentioning

confidence: 79%

“…The issue with automatic face analysis is that traditional machine learning methods deal with race classification only in combination with two separate steps: extracting artificially designed features and training the right classifier with these features. According to Wu et al [65], there are other ways to eliminate these issues.…”

Section: Related Workmentioning

confidence: 99%

A Case Study of Facial Emotion Classification Using Affdex

Magdin

Benko

Koprda

2019

Sensors

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“…As highlighted above ( Andreotti et al, 2017 ; Fan et al, 2018 ; Lai et al, 2019 ; Phukan et al, 2023 ), 1D-CNNs have exhibited their effectiveness in identifying morphological features and comprehending temporal variations in time series data, demonstrating superior capability in AF detection using single-lead ECG signals. However, despite the promising utility of 1D-CNNs in time series analysis, comparative studies in the literature Ullah et al (2021) and Wu et al (2018) indicate that 1D-CNNs often yield lower prediction accuracies than their 2D counterparts under similar network configurations for ECG classification tasks.…”

Section: Research Backgroundmentioning

confidence: 99%

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Xie,

Stavrakis,

Yao

2024

Front. Physiol.

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Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics.Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets.Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model’s superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.

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“…Many experts and organizations now advocate for more careful and ethical approaches to understanding human diversity. Nevertheless, there are many practical applications of race classification such as healthcare and medical research, public health, census and demographics, education, law enforcement, diversity and inclusion initiatives, cultural preservation, historical research, marketing and consumer research, and social services [1].…”

Section: Introductionmentioning

confidence: 99%

Ethnicity Classification: Discriminating Between Iranian and Asian Populations Using Hybrid Deep Learning Algorithm

Ache,

Kassir,

Ibrahim

2023

JIPIRS

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The rapid advancement of deep learning techniques has opened new avenues for solving complex classification problems in various domains. This research explores the application of Convolutional Neural Networks (CNN), specifically the VGG16 architecture, in conjunction with Support Vector Machines (SVM) for the classification of ethnicity using Iranian and Asian facial images. The classification of ethnicity based on facial features is a challenging task due to the subtle and complex variations within and between different ethnic groups. The proposed methodology involves a two-step process. First, the VGG16 CNN model is utilized to extract high-level features from the facial images. The pre-trained VGG16 model, known for its depth and representational power, is fine-tuned on the dataset to capture relevant ethnic features. The extracted features are then fed into an SVM classifier, which is trained to distinguish between Iranian and Asian facial characteristics. A comprehensive dataset consisting of labeled Iranian and Asian facial images is compiled and preprocessed for training and evaluation. The model's performance is assessed using metrics such as accuracy. Various experiments are conducted to optimize hyperparameters and validate the generalization capability of the proposed model. Additionally, visualization techniques are employed to provide insights into the features learned by the CNN and the decision boundaries established by the SVM. The results indicate that the combined approach of CNN-VGG16 and SVM yields promising accuracy in the classification of ethnicity based on facial images.

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Race Classification from Face using Deep Convolutional Neural Networks

Cited by 8 publications

References 5 publications

A Case Study of Facial Emotion Classification Using Affdex

A Case Study of Facial Emotion Classification Using Affdex

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Ethnicity Classification: Discriminating Between Iranian and Asian Populations Using Hybrid Deep Learning Algorithm

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