Proposing an Image Enhancement Algorithm Using CNN for Applications of Face Recognition System (original) (raw)
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Preprocessing Techniques to Improve CNN based Face Recognition System
Computer Science & Information Technology (CS & IT), 2021
In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accurac...
Face Recognition Using CNN Trained With Histogram Equalization Based Image Enhancement Scheme
European Journal of Molecular & Clinical Medicine, 2020
Face recognition is considered as a promising solution for video surveillance systems. Currently, the still image-based face recognition techniques have obtained promising accuracy but detection and recognition of faces in real-time videos has becomes a challenging task. Moreover, the demand of CCTV (Closed-Circuit Television) based surveillance has increased rapidly where the quality of videos is very low. Thus, the poor-quality video, occlusion, and other conditions creates various complexities in face recognition. Currently, CNN (Convolutional Neural Network) based techniques have gained attraction from research community because these techniques have good learning capability and provide better accuracy. In this work, we have introduced CNN (Convolutional Neural Network) based scheme which uses feature extraction and feature embedding modules along with Google Net architecture to improve the learning of CNN (Convolutional Neural Network). We have incorporated histogram equalization-based image enhancement approach to improvise the quality of video frames. The proposed approach is implemented using Python 3.7. The experimental analysis shows that proposed approach achieves the accuracy as 98.55% and AUC(Area under the Curve) as 99.10% for open source datasets whereas for real-time scenarios without occlusion it achieves accuracy as 99.12%, for occlusion scenario it achieves 98.87% classification accuracy.
Deep Neural Network for Human Face Recognition Preprocessing Architecture of DNN used
Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural Networks (CovNets), a type of deep networks has been proved to be successful for FR. For real-time systems, some preprocessing steps like sampling needs to be done before using to CovNets. But then also complete images (all the pixel values) are passed as input to CovNets and all the steps (feature selection, feature extraction, training) are performed by the network. This is the reason that implementing CovNets are sometimes complex and time consuming. CovNets are at the nascent stage and the accuracies obtained are very high, so they have a long way to go. The paper proposes a new way of using a deep neural network (another type of deep network) for face recognition. In this approach, instead of providing raw pixel values as input, only the extracted facial features are provided. This lowers the complexity of while providing the accuracy of 97.05% on Yale faces dataset.
Network Architecture Search for Face Enhancement
ArXiv, 2021
Various factors such as ambient lighting conditions, noise, motion blur, etc. affect the quality of captured face images. Poor quality face images often reduce the performance of face analysis and recognition systems. Hence, it is important to enhance the quality of face images collected in such conditions. We present a multi-task face restoration network, called Network Architecture Search for Face Enhancement (NASFE), which can enhance poor quality face images containing a single degradation (i.e. noise or blur) or multiple degradations (noise+blur+low-light). During training NASFE uses clean face images of a person present in the degraded image to extract the identity information in terms of features for restoring the image. Furthermore, the network is guided by an identity-loss so that the identity information is maintained in the restored image. Additionally, we propose a network architecture search-based fusion network in NASFE which fuses the task-specific features that are e...
Proceedings of the Joint INDS'11 & ISTET'11, 2011
Adaptive image processing in the context of Advanced Driver Assistance Systems (ADAS) is a crucial issue because bad weather conditions lead to poor vision. In a foggy weather, image contrast and visibility are low due to the presence of airlight that is generated by scattering light, which in turn is caused by fog particles. Since vision based ADAS are affected by inadequate contrast, a real-time capable solution is required. To improve such degraded images, a method is required which processes each image region separately. Hence, real-time processing is required, the method is realized with the CNN paradigm which claims the characteristic of real-time image processing. To compare the proposed method with existing state-of-the-art methods the Tenengrad measure is applied. Index Terms-Cellular Neural Network, CLAHE, weather degraded image restoration, real-time image processing, adaptive contrast enhancement.
Image enhancement for face recognition
This paper considers procedures for enhancement of images, which are of low contrast, dark or bad lighting. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. We present image enhancement procedures for face recognition, both wide spread and developed by us.
Efficient Technique on Face Recognition Using CNN
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2021
World is facing a severe health crisis due to the fast transmission of the coronavirus(Covid-19).WHO has issued many guidelines for prevention of the spread of coronavirus. According to WHO, the best way to reduce the spread of coronavirus is by wearing mask in public and crowded areas. In this paper, a 2-stage CNN model is proposed to check whether the people are wearing mask or not. The proposed model is trained and tested on a number of data sets.
Proceedings of the 1st ICEECE & AMF, Vol. 1, No 2, 2021, 2021
Face recognition is crucial in real-world applications like video surveillance, human-computer interaction, and security systems. As one of the most important research issues in computer vision, this biometric authenticating system incorporates a wide range of real human facial characteristics. Problems with Internal covariate shift based on deep learning methods for face recognition systems causes gradient explosion or disappearance, resulting in inappropriate network training, network overfitting, and computational load. This lowers recognition accuracy and slows the network speed. Deep learning techniques for face recognition systems must overcome these difficulties. This research presents a modified Pre-activation Batch Normalization Convolutional Neural Network (PABNCNN), which is characteristic with a batch normalization operation after each convolutional layer in all the four convolutional units. The non-Gaussian rectifier linear unit (Relu) activation function works well with this method. The performance of the proposed models is tested using a new dataset called AS-Darmaset, which was created out of the two public online available databases. The two databases are Caltech 101 Objected Categories and Face Recognition Technology (FERET), respectively. This research compared the convergence behavior of the proposed Pre-activation batch Normalization CNN with that of three distinct CNN models. The Post Activation Batch Normalization CNN, Traditional CNN, Sparse Batch Normalization CNN. The experimental results show that the training and validation accuracy of the proposed Pre-activation BN CNN are up to 100.00% and 99.87%. Post Activation Batch Normalization CNN has an accuracy of 100.00% and 99.81% respectively. Traditional CNN has training and validation accuracy of 96.50%, 98.93% and Sparse Normalization CNN has accuracy of 96.50%. CNN has a training accuracy of 96.25% and a validation accuracy of 97.98%.This result illustrates the regularization effect of Pre-Activation-BN-CNN over the state-of-the-art for face recognition systems.
Design of a Face Recognition System based on Convolutional Neural Network (CNN)
Engineering, Technology & Applied Science Research
Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.
Image Restoration and Enhancement Using Deep Learning
International Journal of Engineering Applied Sciences and Technology
During the process of image acquisition, sometimes images are degraded because of various reasons like low resolution of camera, motion blur, noise etc. This paper presents the work associated with the Image Restoration & Enhancement techniques. The process of recovering degraded image is known as Image Restoration. Image restoration includes denoising image, image inpainting, etc. Here we proposed Convolution Neural Network (CNN) with Median Filter for removing noise, Region filling Exemplar Based Inpainting Algorithm for image inpainting. Image enhancement is one amongst the problem in image processing. Haze, low lighting etc. are the various problems in images. The aim of Image enhancement is to process an image such that result is more suitable than original image for specific application. Here for haze removal we implement dark channel prior algorithm and for lightning low-light image we proposed functions. Image enhancement improves the appearance of the image.