Autonomous Morphometrics using Depth Cameras for Object Classification and Identification
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Autonom Morphometri med Djupkameror för Objektklassificering och Identifiering (Swedish)
Identification of individuals has been solved with many different solutions around the world, either using biometric data or external means of verification such as id cards or RFID tags. The advantage of using biometric measurements is that they are directly tied to the individual and are usually unalterable. Acquiring dependable measurements is however challenging when the individuals are uncooperative. A dependable system should be able to deal with this and produce reliable identifications.
The system proposed in this thesis can autonomously classify uncooperative specimens from depth data. The data is acquired from a depth camera mounted in an uncontrolled environment, where it was allowed to continuously record for two weeks. This requires stable data extraction and normalization algorithms to produce good representations of the specimens. Robust descriptors can therefore be extracted from each sample of a specimen and together with different classification algorithms, the system can be trained or validated. Even with as many as 138 different classes the system achieves high recognition rates. Inspired by the research field of face recognition, the best classification algorithm, the method of fisherfaces, was able to accurately recognize 99.6% of the validation samples. Followed by two variations of the method of eigenfaces, achieving recognition rates of 98.8% and 97.9%. These results affirm that the capabilities of the system are adequate for a commercial implementation.
Place, publisher, year, edition, pages
2013. , 62 p.
Depth Cameras, Classification, Morhometrics, Homography, B-Spline, Eigenfaces, Fisherfaces, Local Binary Pattern Histograms, Nerual Network
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
IdentifiersURN: urn:nbn:se:liu:diva-95240ISRN: LiTH-ISY-EX--13/4680--SEOAI: oai:DiVA.org:liu-95240DiVA: diva2:635227
Subject / course
Computer Vision Laboratory
2013-06-10, Algoritmen, B-huset, Linköpings universitet, 581 83 LINKÖPING, 15:15 (Swedish)
Öfjäll, Kristoffer, M.Sc.Ljunggren, Daniel, Dr.
Alfredsson, Lars-Inge, Dr.