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A study on age estimation of facial images using various CNNs (Convolutional Neural Networks)

다양한 CNN 모델을 이용한 얼굴 영상의 나이 인식 연구

  • Sung Eun Choi
  • 최성은 (한양여자대학교 빅데이터과)
  • Received : 2023.09.05
  • Accepted : 2023.10.16
  • Published : 2023.10.30
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Abstract

There is a growing interest in facial age estimation because many applications require age estimation techniques from facial images. In order to estimate the exact age of a face, a technique for extracting aging features from a face image and classifying the age according to the extracted features is required. Recently, the performance of various CNN-based deep learning models has been greatly improved in the image recognition field, and various CNN-based deep learning models are being used to improve performance in the field of facial age estimation. In this paper, age estimation performance was compared by learning facial features based on various CNN-based models such as AlexNet, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152. As a result of experiment, it was confirmed that the performance of the facial age estimation models using ResNet-34 was the best.

얼굴 영상으로부터 나이를 인식하는 기술의 응용분야가 증가함에 따라 이에 대한 연구가 활발히 진행되고 있다. 얼굴 영상으로부터 나이를 인식하기 위해서는 나이를 표현하는 특징을 추출하고, 추출된 특징으로 나이를 정확하게 분류하는 기술이 필요하다. 최근 영상 인식 분야에서 다양한 CNN 기반 딥러닝 모델이 적용되어 성능이 크게 개선되고 있으며, 얼굴 나이 인식 분야에서도 성능 개선을 위해 다양한 CNN 기반 딥러닝 모델이 적용되고 있다. 본 논문에서는 다양한 CNN 기반 딥러닝 모델의 얼굴 나이 인식 성능을 비교하는 연구를 수행하였다. 영상 인식 분야에서 많이 활용되고 있는 AlexNet, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152를 활용하여 얼굴 나이 인식을 위한 모델을 구성하고 성능을 비교하였다. 실험 결과에서 ResNet-34를 이용한 얼굴 나이 인식 모델의 성능이 가장 우수하다는 것을 확인하였다.

Keywords

Acknowledgement

본 논문은 2023 년도 1 기 한양여자대학교 교내연구비에 의하여 연구됨

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