Extracting face images at a distance, in the crowd, or with a lower resolution infrared camera leads to a poorquality face image that is barely distinguishable. In this work, we present a Deep Convolutional Generative Adversarial Networks (DCGAN) for infrared face image enhancement. The proposed algorithm is used to build a super-resolution face image from its lower resolution counterpart. The resulting images are evaluated in term of qualitative and quantitative metrics on infrared face datasets (NIR and LWIR). The proposed algorithm performs well and preserves important details of the face. The analysis of the resulting images show that the proposed framework is promising and can help improve the performance of image super-resolution generation and enhancement in the infrared spectrum.
Ear biometrics has known an increase of interest from the computer vision research community in recent years. This is mainly because ear geometric features can be extracted in a non-intrusive way, are unique to each individual and does not change over time. Different techniques were proposed to extract ear features in 2D and 3D space and use them in a person recognition system. In this work, we propose Deep-Ear a deep convolutional residual network to perform ear recognition. The proposed algorithm uses a 50 layers deep residual network (ResNet50) as a feature extractor followed by 2 fully connected layers and a final softmax layer for classification. Experimental tests were performed on AMI-DB ear dataset. The obtained top-1 accuracy is equal to 95.67% and a top-3 accuracy is 99.67%. These results show that the proposed architecture is promising in developing a robust feature-free ear recognition technique based on deep learning.
Face recognition is a research area that has been widely studied by the computer vision community in the past years. Most of the work deals with close frontal images of the face where facial structures can be easily distinguished. Little work deals with recognizing faces at a distance, where faces are at a very low resolution and barely distinguishable. In this work, we present a deep learning architecture that can be used to enhance lower resolution facial images captured at a distance. The proposed framework uses Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed architecture works well even in the presence of a small number of images for learning. The new enhanced images are then sent to a face recognition algorithm for classification. The proposed framework outperforms classical enhancement techniques and leads to an increase in the face recognition performance.