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This paper explains the gender recognition system through a human facial image by using the basic method of Principal Component Analysis (PCA) combined with Linear Discriminant Analysis (LDA). PCA+LDA method performance can be improved by improvising the preprocessing techniques such as resizing the image, equalizing the histogram, and removing the variation of the image background by adding oval...
One of the major challenges in face recognition is that related to the differences in orientation or pose, the variations of illumination, the facial expressions, the occlusions and aging. In this paper, we propose an efficient method for face recognition in an uncontrolled environment where we fuse Gabor wavelets and Local Binary Patterns (LBP) in the feature extraction phase. Then, we apply the...
Sparse Representation-based Classification (SRC) is a newly introduced algorithm for face recognition, notable for its robust performance to occlusions and corruptions. Local Binary Patterns (LBP) is a very powerful method to describe the texture and shape of images. In this paper, we propose a novel method for facial expression recognition based on sparse representation of LBP features. Extensive...
This paper presents a novel and efficient face recognition technique based on Local Binary Pattern (LBP) with threshold for resolving traditional LBP's weakness of extracting global features. By setting a threshold to enhance the robustness to noise such as light and extract the global features of face preferably. Combining the local features by LBP with global features as the total features of the...
In this paper we present a novel image representation method which treats images as frequency histograms of salient features. The histograms are computed making use of linear discriminant analysis (LDA). The method employs saliency feature extraction and image binarisation. Then subspace-projected features are extracted. Using the saliency maps as the positive and negative labels, the image features...
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