This paper presents a new approach to model face images using the state space feature parameters. We present a novel feature extraction method for the recognition of face images based on their grayscale images eliminating any step of pre-processing. Experiments are performed using the standard AT & T (formerly, ORL face database) face database containing 400 face images of 40 different individuals. The sate space map and state space point distribution graph drawn for 400 individuals' face image shows the credibility of the method. To show the nonlinear nature of the face images the fractal dimension is also computed from the sate space map of the each face image using the box count method. In the recognition stage we used artificial neural network classifier, and the proposed SSPD feature is found to be promising, and this is the first attempt of this kind in the field of face recognition.
This paper presents a new approach to model face images using the state space feature parameters. We also present a novel feature extraction methodfor the recognition offace images based on their grayscale images eliminating any step of preprocessing. Experiments are performed using the standard AT & T (formerly, ORL face database) face database containing 400 face images of 40 different individuals.The state space map and state space point distribution graph drawn for 400 individuals 'face image shows the credibility ofthe method. To show the nonlinear nature of the face images the fractal dimension is also computed from the state space map of the each face image using the box count method In the recognition stage we used k-NN classifier, and the proposed SSPD feature is found to be promising, and this is the first attempt ofthis kind in the field offace recognition.
This paper presents a novel biologically-inspired and wavelet-based model for extracting features of faces from face images. The biological knowledge about the distribution of light receptors, cones and rods, over the surface of the retina, and the way they are associated with the nerve ends for pattern vision forms the basis for the design of this model. A combination of classical wavelet decomposition and wavelet packet decomposition is used for simulating the functional model of cones and rods in pattern vision. The paper also describes the experiments performed for face recognition using the features extracted on the AT & T face database (formerly, ORL face database) containing 400 face images of 40 different individuals. In the recognition stage, we used the Artificial Neural Network Classifier. A feature vector of size 40 is formed for face images of each person and recognition accuracy is computed using the ANN classifier. Overall recognition accuracy obtained for the AT & T face database is 95.5%.
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