Name
Abstract | ||
|---|---|---|
In this paper, we introduce a modified fuzzy min-max(FMM) neural network model for pattern classification, and present a real-time face detection method using the proposed model. The learning process of the FMM model consists of three sub-processes: hyperbox creation, expansion and contraction processes. During the learning process, the feature distribution and frequency data are utilized to compensate the hyperbox distortion which may be caused by eliminating the overlapping area of hyperboxes in the contraction process. We present a multi-stage face detection method which is composed of two stages: feature extraction stage and classification stage. The feature extraction module employs a convolutional neural network (CNN) with a Gabor transform layer to extract successively larger features in a hierarchical set of layers. The proposed FMM model is used for the pattern classification stage. Moreover, the model is utilized to select effective feature sets for the skin-color filter of the system. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893257_20 | ICONIP |
Keywords | Field | DocType |
feature extraction stage,proposed fmm model,contraction process,larger feature,effective feature set,fmm model,weighted fmm neural network,feature distribution,neural network model,feature extraction module,neural network,selection effect,real time,face detection,feature extraction,gabor transform | Facial recognition system,Pattern recognition,Convolutional neural network,Computer science,Fuzzy logic,Gabor filter,Feature extraction,Artificial intelligence,Face detection,Artificial neural network,Gabor transform | Conference |
Volume | ISSN | ISBN |
4233 | 0302-9743 | 3-540-46481-6 |
Citations | PageRank | References |
3 | 0.49 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ho-Joon Kim | 1 | 41 | 6.48 |
| Juho Lee | 2 | 8 | 1.82 |
| Hyun-Seung Yang | 3 | 49 | 6.48 |