With the continuous development of deep learning, face detection methods have made the greatest progress. For real-time detection, cascade CNN based on the lightweight model is still the dominant structure that predicts face in a coarse-to-fine manner with strong generalization ability. Compared to other methods, it is not required for a fixed size of the input. However, MTCNN still has poor performance in detecting tiny targets. To improve model generalization ability, we propose a Receptive Field Enhanced Multi-Task Cascaded CNN. This network takes advantage of the Inception-V2 block and receptive field block to enhance the feature discriminability and robustness for small targets. The experimental results show that the performance of our network is improved by 1.08% on the AFW, 2.84% on the PASCAL FACE, 1.31% on the FDDB, and 2.3%, 2.1%, and 6.6% on the three sub-datasets of the WIDER FACE benchmark in comparison with MTCNN respectively. Furthermore, our structure uses 16% fewer parameters.

Most of the existing state-of-the-art deep learning algorithms discovers sophisticated representations in huge datasets using Convolutional Neural Networks (CNN) that mainly adopt backpropagation (BP) algorithm as the backbone for training the face recognition problems. However, since decades ago, BP has been debated for causing trivial issues such as iterative gradient-descent operation, slow convergence rate, local minima, intensive human intervention, exhaustive computation, time consuming, and so on. On the other hand, a competitive machine learning algorithm called Extreme Learning Machine (ELM) emerged with extreme fast implementation and simple in theory has overcome the challenges faced by BP. ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence, has been extensively researched in widespread applications. Nonetheless, till date, none of the work of ELM has proved its competency in tackling face verification problem. Hence, in this paper, we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task. We devise and propose a novel and distinguished Hybrid Local Receptive Field based Extreme Learning Machine with DeepID (hereinafter denoted as H-ELM-LRF-DeepID), to discriminate face pairs. Experimental results on the YouTube Faces database, Labeled Faces in the Wild (LFW) and CelebFaces datasets have shed light upon the feasibility and usefulness of the H-ELM-LRF-DeepID in the face verification task.

Benefit from the novel compute unified device architecture (CUDA) introduced by NVIDIA, graphics processing unit (GPU) turns out to be a promising solution for cryptography applications. In this paper we present an efficient implementation for MD5-RC4 encryption using NVIDIA GPU with novel CUDA programming framework. The MD5-RC4 encryption algorithm was implemented on NVIDIA GeForce 9800GTX GPU. The performance of our solution is compared with the implementation running on an AMD Sempron Processor LE-1200 CPU. The results show that our GPU-based implementation exhibits a performance gain of about 3-5 times speedup for the MD5-RC4 encryption algorithm.

Linux Unified Key Setup (LUKS) is a popular disk encryption specification for Linux operating system. It is the first time to implement the operations of LUKS with regard to FPGA architectures, which includes PBKDF2-SHA1 and AES128-ECB cipher. The application of this implementation to LUKS password recovering is verified with ARM processor in Zynq SoC. In order to compare the performance against other platforms, we also implement the algorithm on the NVIDIA GeForce GTX 480 and ATI Radeon R9 200 Series, and multi-core CPU. Our implementation is superior advantage on the energy efficiency index.