https://pcotret.gitlab.io/tags/image/Recent content in Image on Pascal CotretHugoenSun, 13 Sep 2015 00:00:00 +0000https://pcotret.gitlab.io/publications/2015-cotret-spie/Sun, 13 Sep 2015 00:00:00 +0000https://pcotret.gitlab.io/publications/2015-cotret-spie/<p>For several years, face recognition has been a hot topic in the image processing field, this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform, results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).</p>https://pcotret.gitlab.io/publications/2015-cotret-gresti/Tue, 08 Sep 2015 00:00:00 +0000https://pcotret.gitlab.io/publications/2015-cotret-gresti/<p>Dans le domaine du traitement d’images, la reconnaissance faciale est une technique appliquée dans de nombreuses applications, télésurveillance, accès à des zones restreintes, déverrouillage de systèmes électroniques, etc. Dans ce contexte, cette contribution propose une méthode rapide de reconnaissance faciale basée sur la transformée en ondelettes robuste aux variations de position et de luminosité pour des applications temps réel. La méthode proposée a une tolérance de +/- 10% aux variations de position avec des conditions de luminosité variables. Sur une plateforme embarquée type RaspberryPi, le temps de reconnaissance moyen est de 26 ms par visage avec une empreinte mémoire 64 fois plus faible que l’approche de référence et des taux de reconnaissance équivalents.</p>