Face Recognition using Artificial Neural Network and Feature Extraction
Sayan Sarkar, K. B. Ajitha Shenoy
Abstract
Face Recognition is one of the major research areas in Computer Vision. Researchers have applied many image processing techniques and neural networks for the problem but still not able to achieve the desired accuracy for all kinds of data. This work presents a hybrid approach by combining output of two different artificial neural networks PCA-ANN and LDA-ANN. For any given face image, feature extraction techniques have been applied to obtain a representation of the image, using interest point and edge detectors, namely, Harris, SIFT, Canny and Laplacian of Gaussian. Principal Component Analysis and Linear Disciminant Analysis have been actively used for dimensionality reduction of the extracted feature vector. Considering two such different representations, we have trained using an artificial neural network and finally combined the result using a logical OR operation. On Faces94, the proposed approach achieves 98.5% accuracy outshines DeepID and Light CNN-9 approach and fairs significantly better than most state-of-the-art deep learning works.