Improved face detection method at a distance with skin-color and variable edge-mask filtering

Abstract

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

Face identification with frequency domain matched filtering in mobile environments

“…Recently, the correlation-based filter has been adopted for face verification [1][2][3][4][5] . The correlation-based filtering can detect the face region and identify the face simultaneously thus, pre-processing such as face region extraction and alignment is not required for recognition.…”