Abstract—Face detection from a long database of face images with different backgrounds is not an easy task. In this work, we demonstrate the face detection system of colored face images which is invariant to the background and acceptable illumination conditions. A threshold level is set to reject the non-human face images and the unknown human face images which are not present in the input database of face images. In this paper, the global features extraction is completed using DTCWT which provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations and PCA which is on based eigenface computation method. The fusion of local DT-CWT coefficients of detail subbands and PCA coefficients are used to extract the facial features which improve the face recognition and the detection part is completed using multi-layered feed forward Artificial Neural Networks with Feed forward network. This algorithm is implemented using MATLAB software. The learning process of neurons is used to train the input face images with 1000 iterations to minimize the error. In this system, face recognition task is completed with improved accuracy and success rate even for noisy face images.

Index Terms—Keywords: Face recognition system, Dual Tree Complex Wavelet Transform (DTCWT), Principal Components Analyses (PCA), Artificial Neural Network (ANN), Neurons, Epochs, Eigenfaces, Mean Square Error (MSE)

Cite: Vinoda Yaragatti and Bhaskar B, "FACE RECOGNITION USING NEURAL NETWORKS," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 3, No. 3, pp. 124-130, July 2014.