Smart security system for sensitive area using face recognition

Chowdhry, Danish; Hussain, Aqeel; Rehman, M. Z. Ur; Ahmad, Farhan; Ahmad, Arslan; Pervaiz, Mahmood

doi:10.1109/csudet.2013.6670976

Cited by 25 publications

(12 citation statements)

References 4 publications

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“…Jika pada bidang medis pengolahan citra dimanfaatkan untuk mendeteksi penyakit [3] dan pada bidang keamanan digunakan untuk mendeteksi wajah [4][5][6], penulis dalam paper ini memanfaatkan pengolahan citra untuk mendeteksi nomor plat kendaraan dalam format Indonesia dengan metode template matching yang dapat diimplementasikan pada sistem parkir kendaraan.…”

Section: Tujuanunclassified

Detection of Vehicles Number Plate Using Image Processing with Template matching Method

Sarief

Biu²,

Harismana³

et al. 2019

Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan

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To design a system in order to identify an object number plate for the Indonesian format, an initial system is designed, in the form of a vehicle licence plate recognition application using template matching method. The goal of this application is to be implemented to the parking system by identifying the number plate. This system uses the camera for the image capture process, by utilizing image processing technology with the matching correlation template method for recognition to produce a string value from the image. Before doing recognition process, First, the pre processing stage is performed on the input image which includes grayscale, binary, until the segmentation stage before the correlation / comparison process is carried out on the image of Template. The process that occure in the pre-processing unit done for some reason including to make the image lighter and less complex. This process will make the image easer to be processed and also to increase the proses speed of the system. Before aply template matching algorithm to the image output from segmentation process, the image has to be resized first to match the size of the template image stored in data base. This has to done so that the target image and the template image can be match directly with template matching algorithm. The output of this system is a string value which is refer to the value of the license plate capture by camera used by the system. The problem that arises in the introduction process is how to identify various types of characters with various sizes and shapes so that the string value is the same as the text image. The average success rate of this application is 70% so that further research must be carried out so this system can be implemented into the parking system. Keyword : Image Processing, Template matching, Camera, Number Plate, Matlab

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Section: Tujuanunclassified

Detection of Vehicles Number Plate Using Image Processing with Template matching Method

Sarief

Biu²,

Harismana³

et al. 2019

Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan

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“…This step not only improves the detection speed but also decreases the false positives. Background is initialized for BGS by averaging first 20 frames and background is updated continuously to adapt illumination variations [7]. To eliminate scale-space issue, the motion region extracted is resized to make a detection window in which a human can be detected.…”

Section: ) Background Subtractionmentioning

confidence: 99%

Smart home automation system for intrusion detection

Chowdhry

Paranjape

Laforge

2015

2015 IEEE 14th Canadian Workshop on Information Theory (CWIT)

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In the area of home security and protection, visionbased home automation systems (HAS) play a significant role. In this paper, we present the design and implementation of a smart HAS with incorporated intrusion detection to minimize damages caused by burglary. In addition, the proposed HAS integrates a web server to home appliances in order to remotely access and control their status. Intrusion detection, on the other hand, uses Histogram of Oriented Gradients (HOG) feature descriptors and a Support Vector Machine (SVM) classifier for accurate human detection by smartly rejecting false alarms arising from pets. This system comprised of a simple architecture and requires no human intervention. It allows for prompt accessibility, efficient usage of electricity and provides user convenience.

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“…In recent years, researchers have studied intensively on protecting personal data facilitatively. Furthermore, the most relevant issue is facial analysis, which can be applied to facial recognition for high security systems [ 8 , 9 , 10 ], health care [ 11 , 12 ], law enforcement [ 13 , 14 ], etc. Recently, 3D face reconstruction has been a major trend and challenging work in computer vision field, which provides comprehensive face geometry and invariant to illumination compared with 2D imaging.…”

Section: Introductionmentioning

confidence: 99%

3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor

Wang

Peng

2021

Sensors

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Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an effective procedure for facial reconstruction as well as facial recognition via using a depth sensor. For each testing candidate, the depth camera acquires a multi-view of its 3D point clouds. The point cloud sets are stitched for 3D model reconstruction by using the iterative closest point (ICP). Then, a segmentation procedure is designed to separate the model set into a body part and head part. Based on the segmented 3D face point clouds, certain facial features are then extracted for recognition scoring. Taking a single shot from the depth sensor, the point cloud data is going to register with other 3D face models to determine which is the best candidate the data belongs to. By using the proposed feature-based 3D facial similarity score algorithm, which composes of normal, curvature, and registration similarities between different point clouds, the person can be labeled correctly even in a dark environment. The proposed method is suitable for smart devices such as smart phones and smart pads with tiny depth camera equipped. Experiments with real-world data show that the proposed method is able to reconstruct denser models and achieve point cloud-based 3D face recognition.

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Smart security system for sensitive area using face recognition

Cited by 25 publications

References 4 publications

Detection of Vehicles Number Plate Using Image Processing with Template matching Method

Detection of Vehicles Number Plate Using Image Processing with Template matching Method

Smart home automation system for intrusion detection

3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor

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