Improved face recognition method based on segmentation algorithm using SIFT-PCA

• Published in: 2012 35th International Conference on Telecommunications and Signal Processing pp. 758 - 762
• Main Authors: Kamencay, P., Breznan, M., Jelsovka, D., Zachariasova, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2012
• Subjects: ESSEX database; Face; Face recognition; Feature extraction; Graph Based Segmentation; Image segmentation; PCA; Principal component analysis; SIFT; Vectors
• ISBN: 9781467311175; 1467311170
• DOI: 10.1109/TSP.2012.6256399

This paper provides an example of the face recognition using SIFT-PCA method and impact of Graph Based segmentation algorithm on recognition rate. Principle component analysis (PCA) is a multivariate technique that analyzes a face data in which observation are described by several inter-correlated dependent variables. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. The paper presents a proposed methodology for face recognition based on preprocessing face images using segmentation algorithm and SIFT (Scale Invariant Feature Transform) descriptor. The algorithm has been tested on 50 subjects (100 images). The proposed method first was tested on ESSEX face database and next on own segmented face database using SIFT-PCA. The experimental result shows that the segmentation in combination with SIFT-PCA has a positive effect for face recognition and accelerates the recognition PCA technique.