Gender and ethnicity recognition based on visual attention-driven deep architectures

Khellat-Kihel, Souad; Muhammad, Jawad; Sun, Zhenan; Tistarelli, Mássimo

doi:10.1016/j.jvcir.2022.103627

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…AlBdairi et al [33] developed a DCNN-based 2D race recognition approach to determine the human race, the author used highperformance devices to build a a face recognition system and proposed a technique called field-programmable gate arrays (FPGAs). The periocular area is analyzed for race and gender by Khellat-Kihel et al [34] who proved that deep learning techniques in race prediction still require a large amount of labeled data, and accordingly proposed a DCNN-based predictor to solve several specific biometrics issues on the periocular part of the human faces. Periocular regions' features are extracted from 2D faces using different pre-trained architectures such as Alex-net and ResNet-50.…”

Section: Deep Learning In 3d Facial Traits Recognitionmentioning

confidence: 99%

3D Human Facial Traits’ Analysis for Ethnicity Recognition Using Deep Learning

Reda,

Abbas

2024

ISI

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Among different recent technologies proposed for human face classification and recognition, solutions based on analyzing the 3D geometric facial features emerged as a promising academic and practical direction. Researchers have examined both holistic and local approaches to analyzing the 3D face regions to study the impact of facial features in real-life applications such as medical and security implementations. However, a few works have investigated the relevant impact of the extracted geometric features from the descriptive local regions of the human face on identifying human ethnicity. This work proposes a classifier to categorize individuals into their distinctive ethnic groups and deeply analyzes the facial feature variations to highlight the most descriptive parts and features of the human face in race classification. The proposed ML-based classifier is preceded by extracting the 3D facial features from 3D meshes using the recent SIFT and Geodesic distance calculations. In addition, it implements and discusses the initial important preprocessing steps including, cropping the frontal parts, correcting the head pose, selecting the suitable initial key points, aligning the 3D meshes, and implementing the suitable template-based 3D registration. The proposed NN race classifiers are built and evaluated using Headspace, a well-known multi-ethnic dataset, and achieved high accuracy (90% globally, and 100% for the mouth area) especially while using the SIFT features.

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Section: Deep Learning In 3d Facial Traits Recognitionmentioning

confidence: 99%

3D Human Facial Traits’ Analysis for Ethnicity Recognition Using Deep Learning

Reda,

Abbas

2024

ISI

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“…It is a binary classification problem for a permanent human attribute. It has been studied with various biometric modalities, such as fingerprints [1], hand [2], face [3], ears [4], periocular region [5], full-body [6], and oral regions [7]. The research community has been focusing on gender…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Gender Classification by Training With Fake Data

Oulad-Kaddour,

Haddadou,

Vilda

et al. 2023

IEEE Access

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Gender classification of human faces is a trending topic and a remarkable biometric task. This research area has useful applications in several fields, such as automated border control (ABC) and forensic work. There are many approaches to gender classification in the literature; the classical approaches usually use real faces. Although good performances have been achieved, data collection remains a problem. Additionally, the privacy of individuals must be included in many existing works. These drawbacks can be overcome by using fake faces. Recently, the creation of a robust fake face corpus using machine learning has become possible. Our main contribution in the present paper is to experimentally investigate the ability of an artificial deepfake corpus to be a substitute for real corpora in facial gender classification tasks. We propose a deep learning-based approach using convolutional neural networks trained with fake faces and tested on real faces. By exploiting artificial faces, data collection obstacles are resolved for the training step, and privacy is highly preserved. Four classifiers based on popular convolutional neural network architectures were implemented. In the test phase, we used faces of real identities extracted from well-known experimental databases such as Face Recognition Technology (FERET), Faculdade de Engenharia Industrial (FEI) faces, Face Recognition and Artificial Vision (FRAV) and Labeled Faces in the Wild (LFW). The results achieved are very promising. We obtained high accuracy rates and low EER scores. They are similar to those of research works using real faces. As a result of this work, we propose a gender-labeled deepfake facial dataset containing more than 200k deepfake corpora that we will make available upon request for research purposes.

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