On the effect of age perception biases for real age regression

Jacques, Julio C. S.; Özçinar, Çağrı; Marjanović, Marina; Baró, Xavier; Anbarjafari, Gholamreza; Escalera, Sérgio

doi:10.1109/fg.2019.8756595

Cited by 4 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With one study showing a 20% decrease in age estimation error if the model is trained separately on males and females [20]. Furthermore, recent state-of-the-art research [21] in real and apparent age estimation, utilizing the APPA-REAL dataset, sheds light on the significant impact of race on age estimation algorithms. The APPA-REAL dataset exhibits strong imbalances in the race attribute, influencing real age estimation results.…”

Section: Introductionmentioning

confidence: 99%

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

Panić,

Marjanović,

Bezdan

2024

Preprint

View full text Add to dashboard Cite

In the context of artificial intelligence's pervasive integration across sectors, the rigorous examination of methodologies used in constructing mathematical models is imperative. This study delves into facial recognition models, particularly in their ability to extract features such as age. We address the prevalent practice of training models with unbalanced datasets and its implications for model performance and bias introduction. Utilizing 27,305 facial images, we investigate the influence of dataset imbalance on age prediction based on ethnicity. Employing preprocessing and oversampling techniques, we equalize sample sizes across ethnicities. A Convolutional Neural Network trained on the balanced dataset serves as our baseline, while three additional models undergo training with a 50% sample size reduction for each group to assess accuracy degradation. Our findings reveal a notable decline in accuracy for ethnic groups with reduced representation. The Asian group experiences a 67.94% accuracy deterioration, followed by a 51.60% drop for the Black group, and a 38.46% decline for the White group. These results underscore the sensitivity of certain ethnic groups to underrepresentation, highlighting the nuanced impact on age prediction accuracy. While a 50% reduction in representation does not uniformly result in a 50% accuracy decline, it unquestionably influences predictive accuracy for affected groups.

show abstract

Section: Introductionmentioning

confidence: 99%

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

Panić,

Marjanović,

Bezdan

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…They also find that female guessers are generally more accurate at estimating age than male guessers. Jacques et al ( 2019) [18] focus on improving real age estimation by incorporating apparent age and facial attributes (gender, race, happiness, and makeup) into an end-to-end deep learning model. While they do not explicitly address dataset imbalance or rebalancing techniques, their work highlights the importance of considering biases related to facial attributes in age estimation.…”

mentioning

confidence: 99%

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

Panić,

Marjanović,

Bezdan

2024

Preprint

View full text Add to dashboard Cite

In this study, we investigate how dataset composition influences the performance and bias of age estimation models across different ethnic groups. We utilized pre-trained Convolutional Neural Networks (CNNs) such as VGG19, fine-tuning them on two datasets: UTKFace and APPA-REAL. UTKFace comprises 23,705 samples (12,391 males, 11,314 females) including 10,078 White, 4,526 Black, and 3,434 Asian individuals. APPA-REAL consists of 7,591 samples (3,818 males, 3,773 females) with 6,686 White, 231 Black, and 674 Asian individuals. We adjusted dataset composition by oversampling minority groups and reducing samples from the majority group to evaluate effects on model performance, measured using Mean Absolute Error (MAE) and standard deviation. Our findings demonstrate that oversampling alone does not guarantee equitable performance across ethnicities; reducing samples from the majority group often resulted in more balanced performance, evidenced by lower MAE standard deviations. These results underscore the importance of a nuanced approach to achieve fairness, including combining various sampling techniques. This study emphasizes the critical need for tailored dataset compositions to mitigate bias and suggests exploring more refined data processing methods and algorithmic adjustments for enhanced model fairness and accuracy in facial recognition technologies.

show abstract

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Jacques

Güçlütürk

Pérez

et al. 2022

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.

show abstract

On the effect of age perception biases for real age regression

Cited by 4 publications

References 23 publications

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Contact Info

Product

Resources

About