Name
Abstract | ||
|---|---|---|
Effective and real-time face detection has been made possible by using the method of rectangle Haar-like features with AdaBoost learning since Viola and Jones' work [12]. In this paper, we present the use of a new set of distinctive rectangle features, called Multi-block Local Binary Patterns (MB-LBP), for face detection. The MB-LBP encodes rectangular regions' intensities by local binary pattern operator, and the resulting binary patterns can describe diverse local structures of images. Based on the MB-LBP features, a boosting-based learning method is developed to achieve the goal of face detection. To deal with the non-metric feature value of MB-LBP features, the boosting algorithm uses multibranch regression tree as its weak classifiers. The experiments show the weak classifiers based on MB-LBP are more discriminative than Haar-like features and original LBP features. Given the same number of features, the proposed face detector illustrates 15% higher correct rate at a given false alarm rate of 0.001 than haar-like feature and 8% higher than original LBP feature. This indicates that MB-LBP features can capture more information about the image structure and show more distinctive performance than traditional haar-like features, which simply measure the differences between rectangles. Another advantage of MB-LBP feature is its smaller feature set, this makes much less training time. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74549-5_2 | ICB |
Keywords | Field | DocType |
haar-like feature,face detection,original lbp feature,mb-lbp feature,traditional haar-like feature,weak classifier,rectangle haar-like feature,distinctive rectangle feature,multi-block lbp representation,smaller feature set,non-metric feature value,regression tree,false alarm rate,local binary pattern,real time | Computer vision,AdaBoost,Pattern recognition,Computer science,Feature (computer vision),Rectangle,Local binary patterns,Boosting (machine learning),Artificial intelligence,Face detection,Constant false alarm rate,Discriminative model | Conference |
Volume | ISSN | ISBN |
4642 | 0302-9743 | 3-540-74548-3 |
Citations | PageRank | References |
138 | 4.77 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lun Zhang | 1 | 635 | 28.46 |
| Rufeng Chu | 2 | 560 | 27.44 |
| Shiming Xiang | 3 | 2136 | 110.53 |
| Shengcai Liao | 4 | 2582 | 98.34 |
| Stan Z. Li | 5 | 8951 | 535.26 |