Face detection on distorted images by using quality HOG features
Jagruti Mahajan, Ajay Namdeorao Paithane · 2017
Here, evaluate the abasement in execution of well known and effective face detector when human captured picture quality is corrupted by additive gaussian noise and blur. It is observed that, inside a specific scope of recognized picture quality, an adequate increase in picture quality can improve face detection performance. These results can be utilized to guide data transfer capacity which regards with face detection task. A new face detector based on QualHOG features is proposed for robust face detection that increases image indicative Histogram of Oriented Gradients (HOG) features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features. The new detector provides significant improvement in tolerance to image distortion. To improve this research, new face database containing face and non-face patches from pictures by variety of common distortion types and levels were created. Here we used 347 faces and 1287 non-faces database.