Gabor-based Face Recognition with Illumination Variation using Subspace-Linear Discriminant Analysis

            Face recognition has been an active research topic in the past few decades due to its potential applications. Accurate face recognition is still a difficult task, especially in the case that illumination is unconstrained. This paper presents an efficient method for the recognition of faces with different illumination by using Gabor features, which are extracted by using log-Gabor filters of six orientations and four scales. By Using sliding window algorithm, these features are extracted at image block-regions. Extracted features are passed to the principal component analysis (PCA) and then to linear discriminant analysis (LDA). For development and testing we used facial images from the Yale-B databases. The proposed method achieved 86–100 % rank 1 recognition rate

HYBRID ALGORITHM FOR HAND GESTURE RECOGNITION USING LOCAL GABOR FILTER AND MEL FREQUENCY CEPSTRAL COEFFICIENTS

Hand gesture is a movement ofhands having meaning to speak, with other people. However, using hand gestures as a medium for communication requires correct recognition of indeed pose and due to this, hand gesture recognition is an active area of research in the vision community. Various algorithms are proposed for gesture recognition, but not optimally designed for accuracy. Accuracy is the most important parameter for any recognition system as compared to other significant parameters. Increase in accuracy leads to decrease in the performance of other parameters; specifically, it leads the algorithm to high complexity