Combining heterogeneous features for face detection using multiscale feature selection with binary particle swarm optimization

Hong Pan; Si-Yu Xia; Li-Zuo Jin; Liang-Zheng Xia

doi:10.1117/12.901126

23 November 2011 Combining heterogeneous features for face detection using multiscale feature selection with binary particle swarm optimization

Hong Pan, Si-Yu Xia, Li-Zuo Jin, Liang-Zheng Xia

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Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80061Z (2011) https://doi.org/10.1117/12.901126
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

We propose a fast multiscale face detector that boosts a set of SVM-based hierarchy classifiers constructed with two heterogeneous features, i.e. Multi-block Local Binary Patterns (MB-LBP) and Speeded Up Robust Features (SURF), at different image resolutions. In this hierarchical architecture, simple and fast classifiers using efficient MB-LBP descriptors remove large parts of the background in low and intermediate scale layers, thus only a small percentage of background patches look similar to faces and require a more accurate but slower classifier that uses distinctive SURF descriptor to avoid false classifications in the finest scale. By propagating only those patterns that are not classified as background, we can quickly decrease the amount of data need to be processed. To lessen the training burden of the hierarchy classifier, in each scale layer, a feature selection scheme using Binary Particle Swarm Optimization (BPSO) searches the entire feature space and filters out the minimum number of discriminative features that give the highest classification rate on a validation set, then these selected distinctive features are fed into the SVM classifier. We compared detection performance of the proposed face detector with other state-of-the-art methods on the CMU+MIT face dataset. Our detector achieves the best overall detection performance. The training time of our algorithm is 60 times faster than the standard Adaboost algorithm. It takes about 70 ms for our face detector to process a 320×240 image, which is comparable to Viola and Jones' detector.

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Hong Pan, Si-Yu Xia, Li-Zuo Jin, and Liang-Zheng Xia "Combining heterogeneous features for face detection using multiscale feature selection with binary particle swarm optimization", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80061Z (23 November 2011); https://doi.org/10.1117/12.901126

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KEYWORDS

Facial recognition systems

Sensors

Feature selection

Feature extraction

Binary data

Detection and tracking algorithms

Image processing

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