Demographic classification: Do gender and ethnicity affect each other?

Farinella, Giovanna; Dugelay, Jean‐Luc

doi:10.1109/iciev.2012.6317383

Cited by 22 publications

(11 citation statements)

References 23 publications

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“…race sensitive algorithms trained on different demographic cohorts), or by setting up a comprehensive, balanced face database. Gender perception differs across race, indicating the existence of culture-specific stereotypes and concepts of gender [211] (But see [212]). Strong "other-race effect" was found in age estimation [213].…”

Section: From Single To Comprehensive Analysismentioning

confidence: 99%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

159

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Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.

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Section: From Single To Comprehensive Analysismentioning

confidence: 99%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

159

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“…Farinella and Dugelay [12] alleged that ethnicity has no effect on gender classification, adopting a binary ethnic categorization scheme for the experimentation: Caucasian and non-Caucasian. Dwork et al [13] demonstrated the importance of understanding sensitive characteristics such as gender and race, in order to build demographically inclusive models.…”

Section: Face Analysismentioning

confidence: 99%

Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach

Das

Dantcheva

Brémond

2019

Lecture Notes in Computer Science

129

100

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This work explores joint classification of gender, age and race. Specifically, we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment towards classification of named soft biometrics, as well as towards mitigation of soft biometrics related bias. The proposed algorithm achieves promising results on the UTKFace and the Bias Estimation in Face Analytics (BEFA) datasets and was ranked first in the the BEFA Challenge of the European Conference of Computer Vision (ECCV) 2018.

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“…This perceived correlation between these traits has motivated additional work such as that in [59], which investigated if ethnicity-based gender classification can improve the accuracy of three gender estimation algorithms, namely pixel-based, HOG and LBP. The work concluded that joint treatment is not beneficial and that gender and ethnicity can be estimated separately, due to the fact that features used to estimate gender are shared by all ethnic groups and features used for ethnicity classification are present in both female and male faces.…”

Section: Relation Between Age Gender and Racementioning

confidence: 99%

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Dantcheva

Elia

Ross

2016

IEEE Trans.Inform.Forensic Secur.

346

224

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Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

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Demographic classification: Do gender and ethnicity affect each other?

Cited by 22 publications

References 23 publications

Learning Race from Face: A Survey

Learning Race from Face: A Survey

Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

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