Face hallucination based on sparse local-pixel structure

In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facial dataset. Unlike many existing face-hallucination methods such as the from local-pixel structure to global image super-resolution method (LPS-GIS) and the super-resolution through neighbor embedding, where the prior models are learned by employing the least-square methods, our framework aims to shape the prior model using sparse representation. Then this learned prior model is employed to guide the reconstruction process. Experiments show that our framework is very flexible, and achieves a competitive or even superior performance in terms of both reconstruction error and visual quality. Our method still exhibits an impressive ability to generate plausible HR facial images based on their sparse local structures

Valvekaameratel põhineva inimseire täiustamine pildi resolutsiooni parandamise ning näotuvastuse abil

Due to importance of security in the society, monitoring activities and recognizing specific people through surveillance video camera is playing an important role. One of the main issues in such activity rises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this work we are proposing a new system which super resolve the image. First, we are using sparse representation with the specific dictionary involving many natural and facial images to super resolve images. As a second method, we are using deep learning convulutional network. Image super resolution is followed by Hidden Markov Model and Singular Value Decomposition based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iCV-F). The experimental results shows that the recognition rate is increasing considerably after applying the super resolution by using facial and natural image dictionary. In addition, we are also proposing a system for analysing people movement on surveillance video. People including faces are detected by using Histogram of Oriented Gradient features and Viola-jones algorithm. Multi-target tracking system with discrete-continuouos energy minimization tracking system is then used to track people. The tracking data is then in turn used to get information about visited and passed locations and face recognition results for tracked people

Facial image super resolution using sparse representation for improving face recognition in surveillance monitoring

Due to importance of security in the society, monitoring activities and recognizing specific people through surveillance video camera is playing an important role. One of the main issues in such activity rises from the fact that cameras do not meet the resolution requirement for many face recognition algorithm. In order to solve this issue, in this paper we are proposing a new system which super resolve the image using sparse representation with the specific dictionary involving many natural and facial images followed by Hidden Markov Model and Support vector machine based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iFRD). The experimental results shows that the recognition rate is increasing considerably after apply the super resolution by using facial and natural image dictionary

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL