Race estimation with deep networks

Ahmed, Mazida A.; Choudhury, Ridip Dev; Kashyap, Kishore

doi:10.1016/j.jksuci.2020.11.029

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Assessments were made on sketch classification performance and gender verification accuracy based on how many images were taken from each individual. Demographic variables such as categorization, age, ethnicity, and gender have a significant impact on the appearance of the human face, with each category further subdivided into classes such as black and white, male and female, young (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), middle age (30-50), and old (50-70). Most students look more like their peers in their age group than they do those in other age groups.…”

Section: Existing Methodsmentioning

confidence: 99%

“…To train the author's deep convolutional network (R-Net), the authors used the recently developed BUPT Equalized Face dataset, which contains around 1.3 million photos in an uncontrolled environment. The studies in [30][31][32][33][34][35][36][37] were conducted on other datasets, such as UTK and CFD, to verify their validity. Additionally, the race-estimation model, VGG16, is compared to R-Net.…”

Section: Existing Methodsmentioning

confidence: 99%

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Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

Abdulwahid

2023

Applied Sciences

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Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race. Until recently, the majority of the work on face biometrics had been conducted on the problem of person recognition from a photograph. However, other softer biometrics such as a person’s age, gender, race, or emotional state are also crucial. The subject of ethnic classification has many potential uses and is developing rapidly. This study summarizes recent advances in ethnicity categorization by utilizing efficient models of convolutional neural networks (CNNs) and focusing on the central portion of the face alone. This article contrasts the results of two distinct CNN models. To put the suggested models through their paces, the study employed holdout testing on the MORPH and FERET datasets. It is essential to remember that this study’s results were generated by focusing on the face’s central region alone, which saved both time and effort. Classification into four classes was achieved with an accuracy of 85% using Model A and 86% using Model B. Consequently, classifying people according to their ethnicity as a fundamental part of the video surveillance systems used at checkpoints is an excellent concept. This categorization statement may also be helpful for picture-search queries.

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Section: Existing Methodsmentioning

confidence: 99%

Section: Existing Methodsmentioning

confidence: 99%

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

Abdulwahid

2023

Applied Sciences

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“…Different approaches have been proposed for ethnicity classification, focusing on feature representation and accuracy for ethnicity classification, especially on deep-learning architecture [2]. The best state-of-the-art result (SOTA) for ethnicity classification is achieved using solutions based on convolutional neural networks (CNNs) [1,2,4,5]; however, the disadvantage of this deep-learning approach is that it requires high-cost computation resources. Meanwhile, conventional solutions based on handcrafted features have been shown to provide comparable accuracy with low-cost computing demand.…”

Section: Introductionmentioning

confidence: 99%

Compact-Fusion Feature Framework for Ethnicity Classification

2023

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In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnicity classification begins with face detection as a preprocessing process to determine a human’s presence; then, the feature representation is extracted from the isolated facial image to predict the ethnicity class. This study utilized four handcrafted features (multi-local binary pattern (MLBP), histogram of gradient (HOG), color histogram, and speeded-up-robust-features-based (SURF-based)) as the basis for the generation of a compact-fusion feature. The compact-fusion framework involves optimal feature selection, compact feature extraction, and compact-fusion feature representation. The final feature representation was trained and tested with the SVM One Versus All classifier for ethnicity classification. When it was evaluated in two large datasets, UTKFace and Fair Face, the proposed framework achieved accuracy levels of 89.14%, 82.19%, and 73.87%, respectively, for the UTKFace dataset with four or five classes and the Fair Face dataset with four classes. Furthermore, the compact-fusion feature with a small number of features at 4790, constructed based on conventional handcrafted features, achieved competitive results compared with state-of-the-art methods using a deep-learning-based approach.

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