Name
Abstract | ||
|---|---|---|
As an important attribute of human beings, ethnicity plays a very basic and crucial role in biometric recognition. In this paper, we propose a novel approach to solve the problem of ethnicity classification. Existing methods of ethnicity classification normally consist of two stages: extracting features on face images and training a classifier based on the extracted features. Instead, we tackle the problem via using Deep Convolution Neural Networks to extract features and classify them simultaneously. The proposed method is evaluated in three scenarios: (i) the classification of black and white people, (ii) the classification of Chinese and Non-Chinese people, and (iii) the classification of Han, Uyghurs and Non-Chinese. Experimental results on both public and self-collected databases demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46654-5_20 | BIOMETRIC RECOGNITION |
Field | DocType | Volume |
Pattern recognition,Convolution,Computer science,Convolutional neural network,Local binary patterns,Artificial intelligence,Biometrics,Artificial neural network,Classifier (linguistics) | Conference | 9967 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.39 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Wang | 1 | 6 | 0.78 |
| Feixiang He | 2 | 3 | 0.39 |
| Qijun Zhao | 3 | 419 | 38.37 |