2 research outputs found
EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Face Recognition
Automatic recognition of people faces many challenging problems which has experienced much attention due to many applications in different fields during recent years. Face recognition is one of those challenging problem which does not have much technique to solve all situations like pose, expression, and illumination changes, and/or ageing. Facial expression due to plastic surgery is one of the additional challenges which arise recently. This paper presents a new technique for accurate face recognition after the plastic surgery. This technique uses Entropy based SIFT (EV-SIFT) features for the recognition purpose. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. But the EV- SIFT method provides the contrast and volume information. This technique provides better performance when compare with PCA, normal SIFT and V-SIFT based feature extraction
A Color Channel Fusion Approach for Face Recognition
Due to high dimensionality of images or generated
color features, different color channels are usually processed
separately and then concatenated together into a feature vector
for classification. This makes channel fusion a crucial step in
color FR systems. However, existing methods simply concatenate
channel-wise color features without identifying the importance
or reliability of features in different color channels. In this
paper, we propose a color channel fusion (CCF) approach using
jointly dimension reduction algorithms to select more features
from reliable and discriminative channels. Experiments using two
different dimension reduction approaches, two different types of
features on 3 image datasets show that CCF achieves consistently
better performance than color channel concatenation (CCC)
method which deals with different color channels equally.Accepted versio