The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
DEX: Deep EXpectation of Apparent Age from a Single Image
Open accessAbstract
In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture [13] and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the age regression problem as a deep classification problem followed by a softmax expected value refinement and show improvements over direct regression training of CNNs. Our proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face. The CNNs of DEX were finetuned on the crawled images and then on the provided images with apparent age annotations. DEX does not use explicit facial landmarks. Our DEX is the winner (1st place) of the ChaLearn LAP 2015 challenge on apparent age estimation with 115 registered teams, significantly outperforming the human reference.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Multi-column deep neural networks for image classification
Ciresan, D., Meier, U., & Schmidhuber, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248110 · Cited by 3,720
Age and Gender Estimation of Unfiltered Faces
Eidinger, E., Enbar, R., & Hassner, T. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2359646 · Cited by 809
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
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Face Detection without Bells and Whistles
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Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval
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Cited by
VGGFace2: A Dataset for Recognising Faces across Pose and Age
Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00020 · Cited by 3,032
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477
“The IMDB-WIKI dataset consists of 523,051 images from 20,284 celebrities (460,723 images from the Internet Movie Data Base (Imdb) and 62,328 images from Wikipedia) at different ages, labelled with 101 labels (0-100 years, one label per year).”
MethodInfluentialDeep Label Distribution Learning With Label Ambiguity
Gao, B.-B., Xing, C., Xie, C.-W., Wu, J., & Geng, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2689998 · Cited by 508
An All-In-One Convolutional Neural Network for Face Analysis
Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R. (2017) · 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) · doi:10.1109/fg.2017.137 · Cited by 409
Video-based emotion recognition in the wild using deep transfer learning and score fusion
Kaya, H., Gürpınar, F., & Salah, A. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.012 · Cited by 235
Metadata
- Authors
- Rothe, R., Timofte, R., & Gool, L. V.
- Venue
- 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 252-257
- OpenAlex
- W2239239723
- Cited by
- 688
- References
- 12
- FWCI
- 19.81
- Type
- conference paper
- Language
- en
- Open access
- green
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- Rothe, R., Timofte, R., & Gool, L. V. (2015). DEX: Deep EXpectation of Apparent Age from a Single Image. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). https://doi.org/10.1109/iccvw.2015.41
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