Face Detection and Pose Estimation using Triplet Invariants
Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesisAlternative title
Ansiktsdetektering med hjälp av triplet-invarianter (Swedish)
Face detection and pose estimation are two widely studied problems - mainly because of their use as subcomponents in important applications, e.g. face recognition. In this thesis I investigate a new approach to the general problem of object detection and pose estimation and apply it to faces. Face detection can be considered a special case of this general problem, but is complicated by the fact that faces are non-rigid objects. The basis of the new approach is the use of scale and orientation invariant feature structures - feature triplets - extracted from the image, as well as a biologically inspired associative structure which maps from feature triplets to desired responses (position, pose, etc.). The feature triplets are constructed from curvature features in the image and coded in a way to represent distances between major facial features (eyes, nose and mouth). The final system has been evaluated on different sets of face images.
Place, publisher, year, edition, pages
Institutionen för systemteknik , 2002. , 41 p.
Technology, Face Detection, Pose Estimation, Neural Networks, HiperLearn, Triplet Invariants
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-1060OAI: oai:DiVA.org:liu-1060DiVA: diva2:17324
Subject / course
Computer Vision Laboratory