What type of study is this?

This is a Quantitative Study study.

October 5, 2025International Journal of Latest Technology in Engineering Management & Applied ScienceOpen Access

Enhanced Age, Gender, Race Estimation Using Multi-task CNN

View Full Paper

Ask AI

Bookmark

Authors

YCYonggang ChiZhejiang Normal University KBKimenyi Butera John Bosco

Discussion

Member takes

Implication

This research reveals improved age, gender, and race classification in facial images, suggesting significant advancements in demographic estimation technologies.

Key Points

High predictive accuracy was achieved with an MAE of 2.95 for age estimation, improving demographic prediction reliability.
Utilizing real-world datasets like UTKFace and synthetic data enabled robust performance across gender and race classification with accuracies of 98.3% and 93.1%, respectively.
The evaluation included mean absolute error and confusion matrices, emphasizing comprehensive insights into performance among diverse demographic groups.
Future enhancements could involve attention mechanisms and fairness-aware learning to address demographic bias in predictions.

Cite This Study

Chi et al. (2025) studied this question.

synapsesocial.com/papers/68e2537cd6d66a53c247451f https://doi.org/10.51583/ijltemas.2025.1409000046

View Full Paper

Ask AI

Bookmark

Implication

This research reveals improved age, gender, and race classification in facial images, suggesting significant advancements in demographic estimation technologies.

Enhanced Age, Gender, Race Estimation Using Multi-task CNN

Discussion

Member takes

Implication

Key Points

Cite This Study

Enhanced Age, Gender, Race Estimation Using Multi-task CNN

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider