Image Compression Effects in Face Recognition Systems

With the growing number of face recognition applications in everyday life, image- an

Détection de visages en domaines compressés

Ce mémoire aborde le problème de la détection de visages à partir d'une image compressée. Il touche également à un problème connexe qui est la qualité des standards de compression et l'estimation de celle-ci. Ce mémoire est organisé sous la forme d'une introduction générale sur la détection de visages et de deux articles soumis à des conférences internationales. Le premier article propose une amélioration de la méthode classique pour comparer la qualité de deux standards. Le deuxième propose une méthode de décompression spécialisée pour faire fonctionner le détecteur de visages de Viola-Jones dans le domaine compressé

Face Recognition in compressed domain based on wavelet transform and kd-tree matching

This paper presents a novel idea for implementing face recognition system in compressed domain. A major advantage of the proposed approach is the fact that face recognition systems can directly work with JPEG and JPEG2000 compressed images, i.e. it uses directly the entropy points provided by the compression standards as input without any necessity of completely decompressing the image before recognition. The Kd-tree technique is used in the proposed approach for the matching of the images. This algorithm shows improvement in reducing the computational time of the overall approach. This proposed method significantly improves the recognition rates while greatly reducing computational time and storage requirements

Quality Classified Image Analysis with Application to Face Detection and Recognition

Motion blur, out of focus, insufficient spatial resolution, lossy compression and many other factors can all cause an image to have poor quality. However, image quality is a largely ignored issue in traditional pattern recognition literature. In this paper, we use face detection and recognition as case studies to show that image quality is an essential factor which will affect the performances of traditional algorithms. We demonstrated that it is not the image quality itself that is the most important, but rather the quality of the images in the training set should have similar quality as those in the testing set. To handle real-world application scenarios where images with different kinds and severities of degradation can be presented to the system, we have developed a quality classified image analysis framework to deal with images of mixed qualities adaptively. We use deep neural networks first to classify images based on their quality classes and then design a separate face detector and recognizer for images in each quality class. We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page