Optimised denoising sparse autoencoder for the detection of outliers for face recognition
X. Ascar Davix, David Judson, R. Jeba · International Journal of Biometrics · 2023
Face recognition is a challenging research in the area of biometric applications due to the variations of input data such as not well centred faces, different pose, occlusions and poor resolution images. Detection and removal of outliers from the input data is essential to improve the performance of the face recognition algorithm. In this deep learning era, deep networks performed well in image classification. Deep networks extract features automatically from the data and updates the weights to reduce loss function. In this paper, we have presented optimised denoising sparse autoencoder (ODSAE) system to detect and remove the outliers in the input dataset. The autoencoder technique performs well in nonlinear transformations. It deals with convolutional layers for learning and provides meaningful information from the input. Softmax classifier is used for the classification of images. The experiment is carried out on Yale and AR face datasets and the results revealed better accuracy in removing outliers.