Novel Face Detection Method Based on Gabor Features Jie Chen, Shiguang Shan, Peng Yang, Shengye Yan, Xilin Chen, Wen Gao

Outline Face detection survey Existing problem of the system Our solution Experiments Conclusion 2019/1/12

How many faces in this pixture？ Face detection Face detection is to determine whether there are any faces within a given image, and return the location and extent of each face in the image if one or more faces present. How many faces in this pixture？ 2019/1/12

AdaBoost T T T T F F F F Local Feature Local Feature Local Feature All Samples Accepted Samples F F F F Rejected samples P. Viola and M. Jones “Rapid object detection using a boosted cascade of simple features” CVPR 2001 2019/1/12

Boosting and its variations 2019/1/12

Existing problem of the system feature Computation cost false alarms Representation ability Haar low high weak Gabor strong 2019/1/12

Our solution AdaBoost + (Harr+Gabor) Harr features : increase the speed Gabor features : decrease the false alarms. The final strong classifier is consisted of a few hundreds of weak classifiers (Harr+Gabor features). 2019/1/12

Gabor filter 2019/1/12

Gaborface 2019/1/12

The schematic of the proposed method 2019/1/12

Experiments Training set and testing set: 2019/1/12

Some examples of Set1 2019/1/12

The ROC curves for our detectors on the MIT face test set 2019/1/12

Training the Detector 2019/1/12

The ROC curves on the MIT+CMU frontal face test set. 2019/1/12

CAS-PEAL Face Database Contains 99,594 images of 1040 individuals (595 males and 445 females) Varying Pose, Expression, Accessory, and Lighting (PEAL) http://www.jdl.ac.cn/peal/index.html 2019/1/12

Detection rates on CAS-PEAL Data Set Faces False alarms Detection rates Results in each sub-directory Frontal 9029 66 96.42% POSE (within 30o) PD 4998 18 94.74% PM 4993 35 99.78% PU 135 98.06% Total 24018 254 97.08% 2019/1/12

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Demo show 2019/1/12

Thank you very much! END 2019/1/12