Name
Abstract | ||
|---|---|---|
As one of the most important soft biometrics, gender has substantial applications in various areas such as demography and human-computer interaction. Successful gender estimation of face images taken under real-world also contributes to improving the face identification results in the wild. However, most existing gender classification methods estimate gender under well controlled environment, which limits its implementation in real-world applications. In this paper, we propose a new network architecture to combine the coarse appearance features with delicate facial features for gender estimation task. We call this method “coarse and fine” to give a harsh description of the gender estimation process. Trained on the large scale uncontrolled CelebA dataset without any alignment, the proposed network tries to learn how to estimate gender of real-world face images. Cross-database experiments on LFWA and CASIA-WebFace dataset show the superiority of our proposed method. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Soft biometrics,Computer science,Convolutional neural network,Network architecture,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianbo Jiang | 1 | 0 | 0.34 |
| Li Shao | 2 | 10 | 1.40 |
| Zhengxi Liu | 3 | 0 | 0.34 |
| Qijun Zhao | 4 | 419 | 38.37 |