[1]SUN Jinguang,DENG Zhishuo.Local feature facial classification method[J].CAAI Transactions on Intelligent Systems,2017,12(1):104-109.[doi:10.11992/tis.201605021]
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Local feature facial classification method
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 1
Page number:
104-109
Column:
学术论文—机器感知与模式识别
Public date:
2017-02-25
- Title:
- Local feature facial classification method
- Keywords:
- facial classification; round-neighborhood; feature coding; local feature representation; multi classification support vector machineface
- CLC:
- TP391.41
- DOI:
- 10.11992/tis.201605021
- Abstract:
- Considering the problems where the feature points of traditional facial classification algorithms are not located in the position of the actual feature points and are heavily dependent upon the contour curve, a facial contour circular neighborhood local feature expression and a facial classification model were proposed.First, the preliminary facial contour feature points were located and then around the feature points, the triple eight connected round-neighborhood was selected.By calculating a neighborhood level and expanding the neighborhood with the central area between the texture changes, the binary code sequence was generated and the tectonic facial local feature vectors can be created. Then, the faces were classified by designing the OVO-RBF-SVM classification model. The experiment was conducted on the CAS-PEAL face library for facial contour feature discrimination, achieving 94.28% accuracy rate; under the same circumstances, the face-type discrimination methods which are based on the active shape model and jaw curve model were compared, and the accuracy rate raised 6.64% and 6.58%, respectively. To a certain extent, the method proposed in this paper solves the problem where the error increases when the location of the feature points are relatively inaccurate, and at the same time, the original picture information is utilized as much as possible, to ensure the accuracy of the contour feature extraction, which has strong robustness. The experimental results show that this method is suitable for facial classification.
- References:
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1900-01-01