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Found 7 papers, 0 papers with code
A Gabor block based Kernel Discriminative Common Vector (KDCV) approach using cosine kernels for Human Face Recognition
Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method.
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image
Finally the best fitted Hough peaks are extracted from the Hough transformed significant blocks for efficient face recognition.
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition
Firstly, the new morphological features i. e., features based on number of runs of pixels in four directions (N, NE, E, NW) are extracted, together with the GLCM based statistical features and LDP features that are less sensitive to the noise and non-monotonic illumination changes, are extracted from the significant blocks of the gradient image.
Human Face Recognition using Gabor based Kernel Entropy Component Analysis
In this paper, we present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance.
A Face Recognition approach based on entropy estimate of the nonlinear DCT features in the Logarithm Domain together with Kernel Entropy Component Analysis
This paper exploits the feature extraction capabilities of the discrete cosine transform (DCT) together with an illumination normalization approach in the logarithm domain that increase its robustness to variations in facial geometry and illumination.
High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model
Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i. e. a spiral scanning technique has been proposed for better feature selection.
High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach
In this paper, we present a technique by which high-intensity feature vectors extracted from the Gabor wavelet transformation of frontal face images, is combined together with Independent Component Analysis (ICA) for enhanced face recognition.
