Semi self-training beard/moustache detection and segmentation simultaneously
| Autor: | Chenchen Zhu, T. Hoang Ngan Le, Khoa Luu, Marios Savvides |
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| Rok vydání: | 2017 |
| Předmět: |
FERET database
Computer science business.industry Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Facial hair Facial recognition system medicine.anatomical_structure Feature (computer vision) Histogram Signal Processing 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Moustache |
| Zdroj: | Image and Vision Computing. 58:214-223 |
| ISSN: | 0262-8856 |
| DOI: | 10.1016/j.imavis.2016.07.009 |
| Popis: | This paper presents a robust, fully automatic and semi self-training system to detect and segment facial beard/moustache simultaneously in challenging facial images. Based on the observation that some certain facial areas, e.g. cheeks, do not typically contain any facial hair whereas the others, e.g. brows, often contain facial hair, a self-trained model is first built using a testing image itself. To overcome the limitation of that facial hairs in brows regions and beard/moustache regions are different in length, density, color, etc., a pre-trained model is also constructed using training data. The pre-trained model is only pursued when the self-trained model produces low confident classification results. In the proposed system, we employ the superpixel together a combination of two classifiers, i.e. Random Ferns (rFerns) and Support Vector Machines (SVM) to obtain good classification performance as well as improve time efficiency. A feature vector, consisting of Histogram of Gabor (HoG) and Histogram of Oriented Gradient of Gabor (HOGG) at different directions and frequencies, is generated from both the bounding box of the superpixel and the super pixel foreground. The segmentation result is then refined by our proposed aggregately searching strategy in order to deal with inaccurate landmarking points. Experimental results have demonstrated the robustness and effectiveness of the proposed system. It is evaluated in images drawn from three entire databases i.e. the Multiple Biometric Grand Challenge (MBGC) still face database, the NIST color Facial Recognition Technology FERET database and a large subset from Pinellas County database. Detect and segment beard/moustache simultaneouslyUse advantages of both pre-trained model and self-trained modelWork on superpixelPropose an aggregate searching strategy to overcome the limits of landmarkerPropose a new feature that is able to emphasize high frequency information of facial hair |
| Databáze: | OpenAIRE |
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