Increasing Face Recognition Rates Using Novel Classification Algorithms

• Published in: International journal of computers, communications & control Vol. 11; no. 3
• Main Authors: Viriri, Serestina, Lagerwall, Brett
• Format: Journal Article
• Language: English
• Published: Oradea Agora University of Oradea 01.06.2016
• Subjects: Adaptive algorithms; Algorithms; Classification; Face recognition
• DOI: 10.15837/ijccc.2016.3.571
• ISSN: 1841-9836

This paper describes and discusses a set of algorithms which can improve ace recognition rates. These algorithms include adaptive K-Nearest Neighbour, daptive weighted average, reverse weighted average and exponential weighted average. ssentially, the algorithms are extensions to the basic classification algorithm sed in most face recognition research. Whereas the basic classification algorithm elects the subject with the shortest associated distance, the algorithms presented in his paper manipulate and extract information from the set of distances between a est image and the training image set in order to obtain more accurate classifications. he base system to which the algorithms are applied uses the eigenfaces technique or recognition with an adapted Viola and Jones algorithm for face extraction. Most f the algorithms proposed show a consistent improvement over the baseline test.