Binarizing Training Samples with Multi-threshold for Viola-Jones Face Detector

Authors:
Hiroaki Inayoshi;Takio Kurita
Affiliations:
Neuroscience Research Institute, Nat. Inst. of Advanced Industrial Science and Technology (AIST), Japan 305-8568;Neuroscience Research Institute, Nat. Inst. of Advanced Industrial Science and Technology (AIST), Japan 305-8568
Venue:
Neural Information Processing
Year:
2008

Citing 4
Cited 0

Detecting Faces in Images: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection

International Journal of Computer Vision
A Comparison of Affine Region Detectors

International Journal of Computer Vision
Face Hallucination: Theory and Practice

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose an alternative method to Viola-Jones face- detector: in learning stage, we replace each of the gray-scale training images with multiple binary images using multi-threshold; and in detection stage, we use a binarized input-image instead of a gray-scale one. We call this method "TMBMT" (Training by Multiple Binarized samples using Multi-Threshold). Using face images of 1040 individuals from the CAS-PEAL face database, we show that proposed face-detector improves the conventional Viola-Jones facedetectors in terms of numbers of both missed-faces and false-alarms. We also discuss (hypothetical) reasons for the improved performance with (partially) supporting evidence: (1) binarization leads to sharpening of feature distribution; (2) use of multi-threshold leads to better selection of feature location. We speculate these two reasons explain the improved performance.