한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)
- 한국정보처리학회 2023년도 추계학술발표대회
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- Pages.568-570
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- 2023
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
연령, 성별, 인종 구분을 위한 잔차블록 기반 컨볼루션 신경망
Residual Blocks-Based Convolutional Neural Network for Age, Gender, and Race Classification
- Khasanova Nodira Gayrat Kizi (Dept. of Artificial Intelligence Convergence, Graduate School Pukyong National University) ;
- Bong-Kee Sin (Dept. of Artificial Intelligence Convergence, Graduate School Pukyong National University)
- 발행 : 2023.11.02
초록
The problem of classifying of age, gender, and race images still poses challenges. Despite deep and machine learning strides, convolutional neural networks (CNNs) remain pivotal in addressing these issues. This paper introduces a novel CNN-based approach for accurate and efficient age, gender, and race classification. Leveraging CNNs with residual blocks, our method enhances learning while minimizing computational complexity. The model effectively captures low-level and high-level features, yielding improved classification accuracy. Evaluation of the diverse 'fair face' dataset shows our model achieving 56.3%, 94.6%, and 58.4% accuracy for age, gender, and race, respectively.
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