This thesis is motivated by developing a face detection system for detecting faces in distorted images. Interaction between face detection and perceptual image quality is studied and analyzed to develop this robust face detection system. It is observed that accuracy of existing face detection systems are degraded with increase in distortion which is occurred due to many factors like low resolution of cameras, during transmission or storing. These types of distortions are AWGN, G Blur and JPEG. To overcome this problem, a new set of features named QUALHOG (which is a combination of NSS features and HOG features) is proposed for better and accurate face detection which augments Histogram of Oriented Gradients (HOG) features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features. Face detection system based on QUALHOG features shows a great improvement in detecting faces as compared to face detection system based on HOG features. A large set of images are used for experimentation. To facilitate these experiments, a distorted face database (DFD) which contains face and non-face images by a variety of common distortion types and levels is used. This new dataset is available for download and further experimentation and it contains images at 10 distortion levels. Precision and Recall are calculated, Precision versus distortion level and Recall versus Distortion level curves are obtained to show the comparison between HOG and QUALHOG based face detection systems. Furthermore obtained results are compared with known results and presented as AUPR versus Distortion level curves to show the feasibility of FDS. Keywords: Face detection system, Distorted images, Perceptual Quality Aware features, Histogram of Oriented Gradients