Face Recognition Using Different Local Features with Different Distance Techniques

Face Recognition Using Different Local Features with Different Distance Techniques

A face recognition system using different local features with different distance measures is proposed in this paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values, Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local features. Sub partitions are rearranged into vertically and horizontally matric...

Face Recognition Using Different Statistical Features

Human Face Recognition with Different Statistical Features

This paper examines application of various feature domains for recognition of human face images to introduce an efficient feature extraction method. The proposed feature extraction method comprised of two steps. In the first step, a human face localization technique with defining a new parameter to eliminate the effect of irrelevant data is applied to the facial images. In the next step three d...

Analyzing Face Recognition Using Pca and Comparison between Different Distance

iated with each of the eigenvector. This paper he position, PCA Algorithm Principal Component Analysis (PCA) dimensionality reduction technique in which multidimensional data is reduced by extracting the desired number of principal components. Without the need of intense effort PCA provides us with an outline that describes how to reduce a complex data set to a lower dimension to reveal certain...

Face recognition using PCA and different distance classifiers

A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition...