scholarly journals Targeted Face Recognition and Alarm Generation for Security Surveillance using Single Shot Multibox Detector (SSD)

2019 ◽  
Vol 177 (22) ◽  
pp. 8-13
Author(s):  
K. M. ◽  
Maisha Binte ◽  
Nazmus Sakib
Keyword(s):  
Face Recognition ◽  
Single Shot ◽  
Security Surveillance

scholarly journals InnovFaceNet: Deep Face Recognition for Industrial Environments

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Nagarjun Gururaj ◽  
Kanika Batra
Keyword(s):  
Face Recognition ◽  
Criminal Justice ◽  
Real Time ◽  
Intelligent Systems ◽  
Facial Recognition ◽  
Industrial Plant ◽  
The World ◽  
Industrial Environments ◽  
Or Organization ◽  
Security Surveillance

In recent times the usage of intelligent systems have paved way formany applications to be robust and self-reliant. One such popularand vast growing technology is face recognition. Facial Recognitiontechnology is used in security, surveillance, criminal justice systemsand many other multimedia platforms. This work proposes a realtime facial recognition technology which can be used in any industrialsetup eliminating manual supervision, ensuring authorized accessto the personnel in the plant. Due to the recent development ofCOVID-19 pandemic around the world, wearing masks has becomea necessity. Our proposed facial recognition technology identifies aperson’s face with mask or no mask in real time with a speed of20 FPS on a CPU and an F1-score of 95.07%. This makes ouralgorithm fast, secure, robust and deployable on a simple personalcomputer or any edge device at any industrial plant or organization.

scholarly journals A deep learning approach to building an intelligent video surveillance system

2020 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Single Shot ◽  
Convolutional Networks ◽  
Starting Point ◽  
Intelligent Video Surveillance System

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

scholarly journals Single-shot face and landmarks detector

2020 ◽  
Vol 44 (4) ◽  
pp. 589-595
Author(s):  
Y.V. Vizilter ◽  
V.S. Gorbatsevich ◽  
A.S. Moiseenko
Keyword(s):  
Face Recognition ◽  
High Speed ◽  
Recognition Algorithm ◽  
Detection Task ◽  
Single Shot ◽  
High Quality ◽  
Facial Landmark Detection ◽  
One Stage ◽  
Biometric Template ◽  
The Face

Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality.

scholarly journals Implementation of Aadhaar Verified, face Recognition based Security Surveillance

2020 ◽  
Vol 9 (9) ◽  
pp. 415-420
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Human Face ◽  
Element Determination ◽  
Python Language ◽  
Cascade Calculation ◽  
Haar Cascade ◽  
Two Phases ◽  
Security Surveillance ◽  
Human Faces

Security and Authentication is a basic piece of any industry. In Real time, Human face acknowledgment can be acted in two phases, for example, Face discovery and Face acknowledgment. This paper actualizes "Haar-Cascade calculation" to distinguish human faces which are sorted out in Open CV by Python language. Gathering with other existing calculations, this classifier creates a high acknowledgment rate even with shifting articulations, effective element determination and low combination of bogus positive highlights. Haar highlight based course classifier framework uses just 200 highlights out of 6000 highlights to yield an acknowledgment pace of 85-95%.

scholarly journals Powerful processing to three-dimensional facial recognition using triple information

2020 ◽  
Vol 9 (4) ◽  
pp. 326
Author(s):  
Mohammad Karimi Moridani ◽  
Ahad Karimi Moridani ◽  
Mahin Gholipour
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Principal Component ◽  
Support Vector ◽  
The Face ◽  
Principal Component Analysis Algorithm ◽  
The Difference ◽  
Security Surveillance ◽  
The Trinity

<p><span>Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.</span></p>

scholarly journals Vibration Guided Automatic Vision for Enhanced Security

2019 ◽  
Vol 9 (2S) ◽  
pp. 301-306
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Security System ◽  
Single Shot ◽  
Human Intervention ◽  
Alert System ◽  
Security Systems ◽  
Home Security ◽  
Shot Detection

The existing security systems are secure but are not smart enough to handle arbitrary scenarios leading to many false triggers of the alert system. Furthermore, these systems require constant human intervention which isdifficult to achieve.They are also vulnerable as they contain many loopholesand the sensors used are easily manipulatable. The proposed system tries to solve this problem in an efficient and a smart way by the use of sensors, AI and IoT which makes the system robust and resistant againstattacks. The system implements advanced face detection via Single Shot Detection and face recognition via Inception Neural Network for recognition of object in a fast and accurate way. This helps the system act according to the situation, thus preventing any damage to theregion which implements this system. In this work the proposed system is implemented and tested as a Home Security System. The system can also be extended to work in other areas like banks, data hubs, museums etc.The overall accuracy of the system was recorded to be 97.95%.

Face recognition and tracking for security surveillance

2021 ◽  
pp. 1-9
Author(s):  
Sreelu P. Nair ◽  
K. Abhinav Reddy ◽  
Prithvi Krishna Alluri ◽  
S. Lalitha
Keyword(s):  
Face Recognition ◽  
Efficient Solution ◽  
Camera Network ◽  
Mass Gatherings ◽  
Missing Children ◽  
Mobile Cameras ◽  
Security Surveillance ◽  
The City

According to the National Crime Records Bureau, 63,407 children have gone missing in the year 2016, which makes almost 174 children go missing in India every day, out of which only 50% are ever found again. This brings up a need for an efficient solution to trace missing children. The proposed solution uses machine assistance during these search activities with face recognition technologies and can be used for essential development of applications which use CCTV footage across a camera network to identify the person lost. In our solution we use One Shot learning for face recognition to identify stranded people in places such as mass gatherings. The same technology can be used for identification of criminals across the city. The paper also talks about the tracking of people across a network of multiple non-overlapping cameras, with a feature of shifting the target tovehicle, if the target gets into one. The experimentation is performed using mobile cameras and thus, helps in monitoring actions of criminals and finding their hideout.

Face Recognition in Poor-Quality Video: Evidence From Security Surveillance

1999 ◽  
Vol 10 (3) ◽  
pp. 243-248 ◽  
Author(s):  
A. Mike Burton ◽  
Stephen Wilson ◽  
Michelle Cowan ◽  
Vicki Bruce
Keyword(s):  
Face Recognition ◽  
Poor Quality ◽  
Quality Video ◽  
Security Surveillance

scholarly journals SINGLE-SHOT FACE DETECTION AND RECOGNITION BASED ON CNN

2021 ◽  
pp. 11-20
Author(s):  
Yu. V. Vizilter ◽  
V. S. Gorbatsevich ◽  
A. S. Moiseenko
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Input Image ◽  
Fine Tuning ◽  
Single Shot ◽  
Face Database ◽  
Face Images ◽  
Face Detection And Recognition ◽  
Number Of Faces ◽  
Detection And Recognition

The paper proposes an architecture and training method of a deep convolutional neural network for simultaneous face detection and recognition. The implemented approach combines the ideas of SSD (Single Shot Detector) and Faster R-CNN (Region proposal Convolutional Neural Networks) algorithms. Face detection is performed similarly to single-stage detection algorithms, and then a biometric template is built by employing RoI (Region of Interest) pooling layers and using the separate branch of the neural network. Training process includes three stages: pretraining of thebasic CNN for face recognition on face images, fine-tuning by using RoI pooling on in painted face images, adding SSD layers and fine-tuning on face detection. Wherein, at the latter stage, training is performed by using shared layers technology for two databases simultaneously. The main feature of the algorithm is high processing speed, which does not depend on the number of faces in the input image. For example, in case of using ResNet-34 as the core architecture for the algorithm, the required time for detecting faces and building biometric templates on an image with 100 faces is less than 13 ms. For training purposes we use CASIA-WebFace for face recognition task and Wider Face for face detection task. Testing is performed on FDDB (Face Detection Dataset and Benchmark), since this database is closer to practical applications than Wider. As long as the main practical task the developed method is intended for is face reidentification, we use Fei Face DataBase for face recognition quality testing. We obtain TPR (True Positive Rate) = 0.928@1000 on FDDB Face DataBase and FAR (Face Acceptance Rate) = 0.03309@FRR (Face Rejection Rate) = 10–4. Therefore, the proposed algorithm allows solving face detection and reidentification tasks in real time with any number of faces on an input image.

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