Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring

Due to the importance of security in society, monitoring activities and recognizing specific people through surveillance video cameras play an important role. One of the main issues in such activity arises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this paper we are proposing a new system which super resolves the image using deep learning convolutional network followed by the 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 show that the recognition rate is improving considerably after apply the super resolution

Tarsier: Evolving Noise Injection in Super-Resolution GANs

Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve NESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection

Reducible Dictionaries for Single Image Super-Resolution based on Patch Matching and Mean Shifting

A single-image super-resolution (SR) method is proposed. The proposed method uses a generated dictionary from pairs of high resolution (HR) images and their corresponding low resolution (LR) representations. First, HR images and the corresponding LR ones are divided into patches of HR and LR, respectively, and then they are collected into separate dictionaries. Afterward, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary is calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary is passed through an illumination enhancement process. By this technique, the noticeable change of illumination between neighbor patches in the super-resolved image is significantly reduced. The enhanced HR patch represents the HR patch of the super-resolved image. Finally, to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image obtained using bicubic interpolation is calculated. The quantitative and qualitative analyses show the superiority of the proposed technique over the conventional and state-of-art methods

Інтелектуальна система розпізнавання образів на основі згорткових нейронних мереж

Магістерська дисертація на здобуття ступеня «магістр» за освітньо-науковою програмою підготовки «Інтегровані інформаційні системи» на тему «Інтелектуальна система розпізнавання образів на основі згорткових нейронних мереж». Дисертація містить 102 сторінки, 54 рисунки, 3 додатки, 26 джерел. Актуальність. Підвищення точності розпізнавання графічних образів комп’ютером є актуальною темою для побудови сучасних інформаційних систем. Метою магістерської дисертації є підвищення ефективності систем розпізнавання графічних образів, вдосконалення технології комп’ютерного зору. Об`єкт дослідження: графічний образ. Предмет дослідження: інтелектуальна система розпізнавання графічних образів на основі згорткових нейронних мереж. Наукова новизна полягає у підвищенні ефективності розпізнавання графічних образів інтелектуальними системами, а саме – у поєднанні методів попередньої обробки зображення та мінімізації помилки системи. Публікація результатів дисертації. За результатами роботи було опубліковано наукові статті: Ткаченко М. С., Сокульський О.Є. Застосування R-CNN при автоматичному позиціонуванні об’єктів через нейромережевий аналіз графічних даних. Ткаченко М. С., Сокульський О.Є. Принципи організації процедури машинного аналізу на основі згорткової нейромережевої архітектури.Master's dissertation for the degree of "master" in the educational program "Integrated Information Systems" on the topic "Intelligent image recognition system based on convolutional neural networks." The dissertation contains 102 pages, 54 figures, 3 appendices, 26 sources. Topicality. Improving the accuracy of computer image recognition is an important topic for building modern information systems. The aim is to improve the efficiency of graphic recognition systems, and enhance computer vision technology. The object of study - graphic image. Purpose of the study - intelligent graphic image recognition system based on convolutional neural networks. Scientific novelty is to increase the efficiency of graphic image recognition by intelligent systems, namely - in a combination of image pre-processing methods and minimize system error Publication of dissertation results. Based on the results of the work, an articles were published: Tkachenko M. Sokylskyi O. Usage of R-CNN in automatic positioning of objects through neural network analysis of graphic data. Tkachenko M. Sokylskyi O. Principles of organization of machine analysis procedure based on convolutional neural network architecture

A novel face recognition system in unconstrained environments using a convolutional neural network

The performance of most face recognition systems (FRS) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be due to the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classification stages. Furthermore, it is noted that only minimal face recognition issues are typically considered in most FRS, thus limiting the wide applicability of most FRS in real-life scenarios. Thus, it is envisaged that developing more effective pre-processing techniques, in addition to selecting the correct features for classification, will significantly improve the performance of FRS. The thesis investigates different research works on FRS, its techniques and challenges in unconstrained environments. The thesis proposes a novel image enhancement technique as a pre-processing approach for FRS. The proposed enhancement technique improves on the overall FRS model resulting into an increased recognition performance. Also, a selection of novel hybrid features has been presented that is extracted from the enhanced facial images within the dataset to improve recognition performance. The thesis proposes a novel evaluation function as a component within the image enhancement technique to improve face recognition in unconstrained environments. Also, a defined scale mechanism was designed within the evaluation function to evaluate the enhanced images such that extreme values depict too dark or too bright images. The proposed algorithm enables the system to automatically select the most appropriate enhanced face image without human intervention. Evaluation of the proposed algorithm was done using standard parameters, where it is demonstrated to outperform existing image enhancement techniques both quantitatively and qualitatively. The thesis confirms the effectiveness of the proposed image enhancement technique towards face recognition in unconstrained environments using the convolutional neural network. Furthermore, the thesis presents a selection of hybrid features from the enhanced image that results in effective image classification. Different face datasets were selected where each face image was enhanced using the proposed and existing image enhancement technique prior to the selection of features and classification task. Experiments on the different face datasets showed increased and better performance using the proposed approach. The thesis shows that putting an effective image enhancement technique as a preprocessing approach can improve the performance of FRS as compared to using unenhanced face images. Also, the right features to be extracted from the enhanced face dataset as been shown to be an important factor for the improvement of FRS. The thesis made use of standard face datasets to confirm the effectiveness of the proposed method. On the LFW face dataset, an improved performance recognition rate was obtained when considering all the facial conditions within the face dataset.Thesis (PhD)--University of Pretoria, 2018.CSIR-DST Inter programme bursaryElectrical, Electronic and Computer EngineeringPhDUnrestricte