Face recognition with Bayesian convolutional networks for robust surveillance systems (original) (raw)

Deep Learning- Based Surveillance System using Face Recognition

ITM Web of Conferences

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

Real-Time Face Recognition System with Enhanced Security Features using Deep Learning

International Journal of Experimental Research and Review, 2023

Identification of people and mask detection has long been a captivating topic, in terms of research and business. This topic has received increasing attention in recent phases due to the speedy advancement of Artificial Intelligence (AI). Nowadays, a lot of applications, including phone unlocking systems, criminal identification systems, and even home security systems, use face recognition as a common technique. Due to the fact that this method only requires a facial image instead of other dependencies like a key or card, it is more secure. Face detection and face identification are often the first two elements of a human recognition system. Even during COVID-19, it is considered the best way to stop the spread of the COVID-19 virus is by wearing a face mask. The risk of contracting the virus can be reduced by almost 70% only by wearing a face mask. In order to promote community health. This Study aims to produce a highly precise and real-time method that can effectively recognize people and identify non-mask faces in public. When a person stands in front of the device, this application detects the human face automatically using detection, extraction, and recognition algorithms. The proposed work applies the Viola-Jones algorithm for face recognition and the YOLOv5 algorithm for mask detection and classification. When the proposed work is tested, this shows higher accuracy in mask detection which is 92.8%.

Face Tracking and Recognition with Deep Feature in Camera Surveillance System

KỶ YẾU HỘI NGHỊ KHOA HỌC CÔNG NGHỆ QUỐC GIA LẦN THỨ XI NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN

Deep Learning is known as the most powerful technique for handling almost Computer Vision problems. Many state-ofthe-art methods in face recognition focus on recognition from still images. However, tackling still images are challenged by uncontrolled conditions such as head pose, motion blur, occlusion. Nevertheless, in the context of camera surveillance system, faces appear in a series of frames containing more unemployed information. In this paper, we propose a method of combining face tracking and face representation for still images to increase high accuracy rate in the setting of camera surveillance. We evaluated different set comparison techniques on video surveillance dataset (ChokePoint), we observe that our combination method accuracy is 98.87% significantly improvement to only using face representation for still images accuracy is 90.25%. We also collect dataset which is more challenges such as far distance, angle view of camera high and achieve accuracy is 71.96% much higher to only using face representatives is 60.55%

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.

AN EFFECTIVE IMPLEMENTATION OF FACE RECOGNITION USING DEEP CONVOLUTIONAL NETWORK

JOURNAL OF SOUTHWEST JIAOTONG UNIVERSITY, 2019

Human Face Recognition for forensic investigations and e-governance is widely adopted so that the specific face points can be trained and further investigations can be done. In this approach, the key points of human face with the dynamic features are extracted and trained in the deep neural network model so that the intrinsic aspects of the human face can be realized and further can be used for the criminal investigation or social analytics based applications. In this research manuscript, the usage of deep learning based convolutional network is integrated for the human face analytics and recognition for diversified applications. It is done to have the cavernous evaluation patterns in multiple domains for the knowledge discovery and predictive features of the human face identification domain.

FACE DETECTION AND RECOGNITION FROM VIDEOS USING CASCADED DEEP LEARNING AND BAYESIAN LEARNING TECHNIQUE

IAEME PUBLICATION, 2021

Now a days, security based applications are developed widely and these systems are adopted in various real-time applications. Visual surveillance is considered as a most promising technique where certain objects can be detected, tracked and recognized using computer vision based approaches. In this field, face detection and recognition is considered as the important part of surveillance system. Several approaches have been developed for face recognition but existing approaches are applied on the face data. Recently, video face detection techniques are also introduced which provides more information to improve the security system. Deep learning achieves substantial improvements in face detection. However, the existing methods need to input fixed-size images for image processing and most methods use a single network for feature extraction, which makes the model generalization ability weak. In response to the above problems, our framework leverages a cascaded architecture with three stages of deep convolutional networks to improve detection performance. The network can predict face in a coarse-to-fine manner. The proposed method takes the Three-Patch Local Binary Pattern (TPLBP) texture feature which has excellent performance in face analysis as the input of the network. The learning process is developed using Bayesian learning approach is developed. The proposed approach is implemented on benchmark datasets such as IARPA Janus Benchmark A (IJB-A), the YouTube Face dataset and the Celebrity-1000 dataset. A comparative performance is carried out which shows the robust performance of proposed approach.

Face Recognition Using Deep Learning

Innovative Scientific Research Publisher, 2020

Today face recognition and its usage are developing at a remarkable rate. Researches are at present building up different strategies in which facial recognition framework works. In circumstances like accidents, normal disasters, missing cases, clashes between nations, kidnappings and numerous different circumstances individuals are regularly isolated by their families. Recognizing the relatives of those refugees is essential to arrive at their family for refugee's security and backing. Everyday polices are enrolling with missing cases, a portion of those enlisted cases are getting tackled and some are definitely not by using the manual method where it takes more time. The goal of this paper is to provide a solution to overcome time delay from existing strategies for police examination utilizing most recent innovation. Hence we adopt a framework which utilizes CNN (Convolutional Neural Network) technique with VGG16 architecture where we use our raw dataset which contains 84 images collected from 21 families data, after applying augmentation method the image count in final dataset is increased to 1512, then from this dataset 80% of data is used for training data and 20% is used for testing data. This framework helps to verify an individual's trait using their face and family subtleties with related model with increased accuracy, and gives a effective solution for identifying refugee's family.

西南交通大学学报 AN EFFECTIVE IMPLEMENTATION OF FACE RECOGNITION USING DEEP CONVOLUTIONAL NETWORK

JOURNAL OF SOUTHWEST JIAOTONG UNIVERSITY, 2019

A Proposed Framework: Face Recognition With Deep Learning

International Journal of Scientific & Technology Research, 2020

Face recognition is the capability to ascertain the identification of a person solitary or amidst multitudes of individuals. In lieu to this, deep learning has dominated and it has been used in recent years due to its momentous performance to solve the face recognition challenges using convolutional neural networks (CNN). It is a technology with enormous capabilities and diversities used in computer vison problems such as modelling and saliency detection, semantic segmentation, handwriting digital recognition, emotion recognition and many more. CNN architectures such has Alex Net, VGG are the practically known architectures that have immensely prompt new dataset for CNN model designs. This paper contributes to actualization of a propose CNN based on a pre-trained VGG Face for face recognition from set of faces tracked in video or image capture achieving a 97% accuracy. Also, implementing the use of metric learning to actualized a discriminative feature from our instances.

Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

Future Internet

In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in...

Face recognition with Bayesian convolutional networks for robust surveillance systems (original) (raw)

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