Real-time Gender Recognition based on
文献类型：会议论文
| 作者 | Yimin Zhou; Zhifei Li |
| 出版日期 | 2016 |
| 会议名称 | IECON2016 |
| 会议地点 | 意大利佛罗伦萨 |
| 英文摘要 | This paper proposed a novel image processing method combining Principal Component Analysis (PCA) and Genetic Algorithm (GA) to reduce the interference of facial expression, lighting or wear but extracting gender feature from frontal face. The collected facial images are first cropped and aligned automatically, then the gray-level information can be converted to feature vectors via PCA. After eigen-features are extracted with high classification performance by the aid of GA, the neural network classifier can be trained accordingly. Compared to the classification methods based on global graylevel information, the obtained classifier has better identification rate but half less used feature dimension, so the calculation load can substantially be reduced during training and identification procedures, which benefits to the development of a real-time identification system. Furthermore, FERET dataset and FEI dataset are used to validate the generality of the proposed method, where 92% and 94% accuracy rates of the gender recognition can be achieved respectively. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10085]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Yimin Zhou,Zhifei Li. Real-time Gender Recognition based on[C]. 见:IECON2016. 意大利佛罗伦萨. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。