Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild

Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras limited capabilities, it is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features. The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms

A Multi-Algorithm, High Reliability Steganalyzer Based on Services Oriented Architecture

In this prospectus we are proposing to develop a unified Steganalyzer that can not only work with different media types such as images and audio, but further is capable of providing improved accuracy in stego detection through the use of multiple algorithms running in parallel. Our proposed system integrates different steganalysis techniques in a reliable Steganalyzer with distributed and Services Oriented Architecture (SOA). The distributed architecture not only allows for concurrent processing to speed up the system, but also provides higher reliability than reported in the existing literature. The extendable nature of the SOA implementation allows for easy addition of new Steganalysis algorithms to the system in terms of services. The universal steganalysis technique proposed in this prospectus involves two processes; feature extraction and feature classification. Three methods are used for feature extraction; Mel-Cepstrum and Markov (for audio), and Intra-blocks for (JPEG images). The feature classification process is implemented using neural network classifier. The unified steganalyzer is tested for JPEG images and WAV audio files. The accuracy of classification ranges from 96.8% to 99.8% depending on the object type and the feature extraction method. In particular, an enhancement of Mel-Cepstrum technique is proposed that achieves an accuracy of 99.8%. This is significantly better than detection accuracy of 89.9% to 98.6% [Liu 2011] where even a much larger training dataset was used than ours

Review of Deep Learning Algorithms and Architectures

Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data sets. In many cases, depending on the problem complexity, DL can also overcome the limitations of earlier shallow networks that prevented efficient training and abstractions of hierarchical representations of multi-dimensional training data. Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures. This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time. We delve into the math behind training algorithms used in recent deep networks. We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others.https://doi.org/10.1109/ACCESS.2019.291220

Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey

Variational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs.https://doi.org/10.1109/ACCESS.2020.304830

Improved Eigenface Recognition Using Hierarchal Technique

Face recognition is one of the important fields of computer vision and pattern recognition because of its applications in security and intelligence systems. In this paper we improve one of the famous algorithms used for face recognition which is Eigenface technique. The method of improvement used in this paper is the Hierarchical technique which means that input image will be applied into different levels of recognition instead of only one level as used in the regular Eigenface method. The hierarchical technique developed in this paper increases the detection rate in the case of large benchmark image database by more than 30% as compared to the original method

3D Hand Pose Detection

Hand pose detection is one of the new important research fields, especially because of its applications in the field of human machine interaction. It benefits deaf and mute people, who cannot use voice recognition systems. The main target of this paper is to design an intelligent, autonomous system that can be used for hand pose detection. The input to the system is from Microsoft Kinect's sensor for depth and color images, and the output is the hand pose that optimally matches the hand in the input image

Source Anonymity in WSNs against Global Adversary Utilizing Low Transmission Rates with Delay Constraints

Wireless sensor networks (WSN) are deployed for many applications such as tracking and monitoring of endangered species, military applications, etc. which require anonymity of the origin, known as Source Location Privacy (SLP). The aim in SLP is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic in the network. Previous approaches to SLP such as Fortified Anonymous Communication Protocol (FACP) employ transmission of real or fake packets in every time slot, which is inefficient. To overcome this shortcoming, we developed three different techniques presented in this paper. Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) were developed to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Most of the current techniques try to prevent the adversary from perceiving the location and time of the real event whereas our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques provide reasonable delivery ratio, delay, and overhead of a real event's packets while keeping a high level of anonymity. Three different analysis models are conducted to verify the performance of our techniques. A visualization of the simulation data is performed to confirm anonymity. Further, neural network models are developed to ensure that the introduced techniques preserve SLP. Finally, a steganography model based on probability is implemented to prove the anonymity of the techniques.https://doi.org/10.3390/s1607095

Optimizing Deep CNN Architectures for Face Liveness Detection

Face recognition is a popular and efficient form of biometric authentication used in many software applications. One drawback of this technique is that it is prone to face spoofing attacks, where an impostor can gain access to the system by presenting a photograph of a valid user to the sensor. Thus, face liveness detection is a necessary step before granting authentication to the user. In this paper, we have developed deep architectures for face liveness detection that use a combination of texture analysis and a convolutional neural network (CNN) to classify the captured image as real or fake. Our development greatly improved upon a recent approach that applies nonlinear diffusion based on an additive operator splitting scheme and a tridiagonal matrix block-solver algorithm to the image, which enhances the edges and surface texture in the real image. We then fed the diffused image to a deep CNN to identify the complex and deep features for classification. We obtained 100% accuracy on the NUAA Photograph Impostor dataset for face liveness detection using one of our enhanced architectures. Further, we gained insight into the enhancement of the face liveness detection architecture by evaluating three different deep architectures, which included deep CNN, residual network, and the inception network version 4. We evaluated the performance of each of these architectures on the NUAA dataset and present here the experimental results showing under what conditions an architecture would be better suited for face liveness detection. While the residual network gave us competitive results, the inception network version 4 produced the optimal accuracy of 100% in liveness detection (with nonlinear anisotropic diffused images with a smoothness parameter of 15). Our approach outperformed all current state-of-the-art methods.http://dx.doi.org/10.3390/e2104042

Super Resolution and 3D Alignment Effects on Unsupervised Face Recognition in the Wild

Majority of face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. It is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subjected for testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features