Face Recognition Using Discrete Cosine Transform for Global and Local Features

TL;DR

This paper proposes a face recognition method using Discrete Cosine Transform to extract both global and local features from frontal face images, aiming to improve recognition accuracy by combining these features.

Contribution

The novel approach combines global and local DCT features with weighted fusion to enhance face recognition performance.

Findings

Improved recognition accuracy by combining features.

Reduced false acceptance rate through feature weighting.

Effective use of DCT for both global and local facial features.

Abstract

Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves recognizing the corresponding face image from the database. The face image obtained from the user is cropped such that only the frontal face image is extracted, eliminating the background. The image is restricted to a size of 128 x 128 pixels. All images in the database are gray level images. DCT is applied to the entire image. This gives DCT coefficients, which are global features. Local features such as eyes, nose and mouth are also extracted and DCT is applied to these features. Depending upon the recognition rate obtained for each feature, they are given weightage and then combined. Both local and global features are used for comparison. By comparing the ranks for global and local features, the false acceptance rate for DCT can be minimized.