Demographic Estimation from Face Images: Human vs. Machine Performance
By Hu Han, Charles Otto, Xiaoming Liu, Anil K. Jain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
321 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/6f793c4bc6bb5bc2da7a61518021b48ccb928be9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Shiguang Shan
- Xilin Chen
- Hu Han
- Abdelrahman Abdelhamed
- Mahmoud Afifi
- Anil K. Jain
- Luc Van Gool
- Xiaogang Wang
In Signal Processing
- Xilin Chen
- Shiguang Shan
- Hu Han
- Mahmoud Afifi
- Abdelrahman Abdelhamed
- Anil K. Jain
- Christin Jose
- Vivek K. Singh
Citations in the corpus
- AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces (2017, methodology): “For the sake of fair comparison, we report only results of methods using unconstrained types of images [16, 9, 41, 42, 43, 8, 18, 12] and we omit results of methods using only frontal or near-frontal face images.”
- Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach (2017, methodology): “MORPH II are reported with a five-fold, subject-exclusive cross-validation protocol [16], [17].”
- Toward Multimodal Cyberbullying Detection (2017, background): “Just as in the early days of natural language processing, the accuracy of these individual detectors varies greatly (typically ranging from 40% to 96%, depending on the criteria used) [22, 3, 23].”