Levenberg-marquardt backpropagation neural network with techebycheve moments for face detection

Abstract

Face detection is an intelligent approach used in a variety of applications that identifies human faces in digital images. This work presents a new method which composes of a neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, in which a discrete Tchebychev transform is given for different sampling patterns and several samples here were performed on color images. A Levenberg-Marquardt backpropagation neural network was applied to the transformed image to find faces in the face detection dataset and FDDB benchmarked database. Model performance was measured based on its accuracy and the best result from the newly proposed method was 98.9%. Simulation results showed that the proposed method handles face detection efficiently.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

“…Feature extraction allows for a more-accurate detection of features, as a result of the image's enhancements [15], [16], [39], [89], [143]- [145], [148]. The signature of feature classes could be distinguished from complex surface textures.…”