Abstract
Face recognition systems have been in the active research in the area of image processing for quite a long time. Evaluating the face recognition system was carried out with various types of algorithms used for extracting the features, their classification and matching. Similarity measure or distance measure is also an important factor in assessing the quality of a face recognition system. There are various distance measures in literature which are widely used in this area. In this work, a new class of similarity measure based on the Lp metric between fuzzy sets is proposed which gives better results when compared to the existing distance measures in the area with Linear Discriminant Analysis (LDA). The result points to a positive direction that with the existing feature extraction methods itself the results can be improved if the similarity measure in the matching part is efficient.
Highlights
The concept of similarity is fundamentally important in almost every scientific field
We propose a novel approach to derive the similarity between two images using new similarity measure, by representing each numerical value of their feature vectors as a fuzzy set, instead of a single value
Feature extraction was done with the renowned Linear Discriminant Analysis method
Summary
The concept of similarity is fundamentally important in almost every scientific field. (2015) Study of Similarity Measures with Linear Discriminant Analysis for Face Recognition. We propose a novel approach to derive the similarity between two images using new similarity measure, by representing each numerical value of their feature vectors as a fuzzy set, instead of a single value. This representation takes into account the uncertainty presents in the extraction process of features and increases the precision rate in the image retrieval process. The rest of this paper is organized as follows: Section 2 presents the mathematical foundations similarity measures on digital images.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
