Face recognition performance using linear discriminant analysis and deep neural networks
Xhevahir Bajrami, Blendi Gashi, Ilir Murturi · International Journal of Applied Pattern Recognition · 2018
The face recognition applications deal with large amounts of images and remain difficult to accomplish due to when displayed with images taken in unlimited conditions. Linear discriminant analysis (LDA) is a supervised method that uses training samples to obtain the projection matrix for feature extraction, while deep neural networks are trainable for supervised and unsupervised tasks. In this paper, we present our results of experiments done with linear discriminant analysis (LDA) and deep neural networks (DNN) for face recognition, while their efficiency and performance are tested on labelled faces in the wild (LFW) dataset. We used two methods of DNN, k-nearest neighbours algorithm (k-NN) and support vector machine (SVM). Experimental results show that the DNN method achieves better recognition accuracy and recognition time is much faster than the LDA method in large-scale datasets. Deep learning methods have shown high accuracy even for images coming out of the dataset.