Hierarchical and discriminative bag of features for face profile and ear based gender classification
| Autor: | Yunhong Wang, Guangpeng Zhang |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Biometrics
Contextual image classification business.industry Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Machine learning computer.software_genre Facial recognition system Support vector machine ComputingMethodologies_PATTERNRECOGNITION Discriminative model Bag-of-words model in computer vision Histogram Artificial intelligence business computer |
| Zdroj: | IJCB |
| Popis: | Gender is an important demographic attribute of human beings, automatic face based gender classification has promising applications in various fields. Previous methods mainly deal with frontal face images, which in many cases can not be easily obtained. In contrast, we concentrate on gender classification based on face profiles and ear images in this paper. Hierarchical and discriminative bag of features technique is proposed to extract powerful features which are classified by support vector classification (SVC) with histogram intersection kernel. With the output of SVC, fusion of multi-modalities is performed at the score level based on Bayesian analysis to improve the accuracy. Experiments are conducted using texture images of the UND biometrics data sets Collection F, and average classification accuracy of 97.65% is achieved, which is comparable to the state of the art. Our work can be used in cooperate with existing frontal face based methods for accurate multi-view gender classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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