Convolutional Autoencoder versus Common Dimensionality Reduction Algorithms for Face Recognition

Face recognition is a state-of-the-art widely employed in areas where security is a concern. The technology consists of two steps namely feature extraction and face classification. In this paper, different dimensionality reduction algorithms such as Convolutional Autoencoder (CAE), Principal Component Analysis (PCA), Polynomial Kernel-Principal Component Analysis (KPCA) and Independent Component Analysis (ICA) were explored and compared on the LFW dataset. After the face features were extracted by the mentioned techniques, a Support Vector Machine (SVM) was used to perform face recognition by classifying the images into 7 groups. It was found out that the accuracy of the SVM was highly dependent on the feature extraction technique employed, with the CAE proving to be the most superior algorithm leading to a classification accuracy of 81.9%. The ICA was the next best performing technique followed by the KPCA and finally by the PCA.