Fuzzy Lattice Neural Model and Fuzzy Equivalance Relation Model

Computer vision, unlike humans, still has not fully acquired the ability to categorise a person's age group from an image of the person's face. Successful gender and age classification could be used to boot the performance of face recognition system. Fuzzy models have been used and analysed in this work to achieve the desired results. The concept of fuzzy lattice neural model is introduced and is applied to classify the age group of a person from the gray scale facial image. Next, the fuzzy equivalence relation model is constructed and is used to classify the age group of a person. Then, the fuzzy lattice neural model is applied to segment an aerial gray scale image. Finally, fuzzy lattice neural model is compared with other models like fuzzy equivalence relation model, Kohenen's clustering neural model that are used for classification. The hybridization of the technologies is demonstrated on architectures such as Neuro-fuzzy systems. This book should be of interest to researchers and technologists desirous of applying FLN technology to their respective fields of work.

D.Kalamani is Associate Professor, Department of Mathematics at the Kongu Engineering College, Erode, Tamilnadu, India. She is a a gold medalist from Bharathiar University. She has over 15 years of teaching experience. Her research interest includes Lattice Theory, Fuzzy Systems, Image Processing, Pattern Recognition and Neural Network.