Published Paper Details:

However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realize facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In this paper, we propose a novel face verification algorithm, which starts with selecting feature-rich frames from a video sequence using Multi-wavelet transform and entropy computation. Frame selection is followed by representation learning-based feature extraction, where three contributions are presented: 1) deep learning architecture, which is a combination of stacked denoising sparse autoencoder (SDAE) and deep Boltzmann machine (DBM); 2) formulation for joint representation in an autoencoder; and 3) updating the loss function of DBM by including sparse and low rank regularization. Finally, a multilayer neural network is used as the classifier to obtain the verification decision. The results are tested on the YouTube Databases.