A Face Detection of People by Using Computer Vision and IoT for Security Application Abstract

A Face Detection of People by Using Computer Vision and IoT for Security Application
In Recent years, many supervision methods can monitor the actual time information and detecting the object that is visible in its. Various supervision system working successfully on the market, it must, above all, provide reliable and precise motion detection. Automation of a home is a trending field for security applications. This area has developed new techniques like Internet-of-Thing, computer vision and many more. Raspberry Pi 3 is the first 64bit version and they have inbuilt features of Bluetooth and Wi-Fi and the size is like a debit card was used at this system and the camera is connected by the Raspberry Pi 3. Basically, In IOT the system is connected and controlled the gadgets to a central hub or the “gateways”. The system is controlled by the User interface by the medium of Tablets, Desktop computer, the mobile application either a web Interface. In this study, blend of IOT with Computer vision for detecting the people that are visible to the cameras and it is helpful for us to identify that specific person. Afterward, human identity in the image they detected the face and captured it as an image and sent the image that contains the face and appearance time to the mobile or tablets by using web gateways in the form of TEXT message. At that time the user can check the image and the details verifying the person and they have the control to permit or denied there entrains.

Keyword: Internet-of-Thing (IOT), Computer Vision, Face Detection, Raspberry PI 3, Web Gateways
Nowadays, Home automation system is a scientific solution that allows automating the bulk of digital, static and technology-based tasks within a residence. It uses a blend of computer hardware and computer software techniques which permit management and supervision through equipment and devices within a home. At best, the movement detector has a small form factor and a nominal price point. It also needs to work indoors and outdoors, be programmable and offer the right connectivity. The latter enables it to trigger an alarm or convey with other tools, such as email or text message, to announce the user if the system detects motion. Lastly, energy-efficiency is and will continue to be a big topic in the fields of home and building mechanization and smart homes.

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The trends such as the Internet-of-Things (IoT) and Computer Vision can increase the accuracy and time Complexity. Many of these – from small household appliances through huge communication networks to complex, industrial automation systems are controlled by special-purpose, embedded computing systems or manage by the Android App/Websites.

Fig 1: Basic idea of CCTV and Face Detection
As Fig 1 Shows a generalized working of the System which consist of following Components
CCTV Camera
It is used for Video supervision and it is nothing but a video camera that primarily, use for transfer of a signal to a definite place, they are not openly transmitted. For transmitting them use generally two methods and they are Point to Point and Point to Multipoint by the connection of mesh wired or wireless links.
Image Preprocessing
It’s used for improved the image quality or extract some beneficial data/information into it. They give out as attributes or features with that image. Basically, it can be analyzing and manipulating the image which includes data compaction and enhancement that are made more visible for human eyes. The main purposes of image preprocessing are visualization, Image Sharpening, Image retrieval, Measurement of pattern and Image Recognition.
Face Detection
Human faces is a dynamic object and it is difficult to detect for computer vision. They first trace the face into the video if any available then. Generally, They try to tract the human face onto the digital image and the detection process is divided into three parts and they are recognize a face to track, identify face to track and then we can track down the face.
This domain mainly consists of Severs and surveillance applications, which help the user by giving him alerts about the visitors that can come to the home.
Default password and Username Problems:
A report that shows certain surveillance cameras can have their predefined user Id and password that’s why the private videos streams from cameras are open to all on the internet and those unsecure Cameras will leak your private information for everyone to view, which is pretty dangerous. In case you cannot change the username and problem then it is quite risky, that other person can access your private information.

Security Camera Pictures Visibilities Problems:
In case, security cameras image quality difficulty occur and they are not able to detect the particular person who can come then it is also a very big loophole. They have various solutions for that problem but, in such a way that kind of issue is handled and maintained by human efforts. And the general, issue are:
a) Picture is Too Bright: This problem occurs when the sun rays or any kind of reflection can come on the cameras. Then is enlarge the shine of the image and the image is not clearly visible.

b) Picture is Too Dark: When the contracts and brightness get decrease then the camera image goes darker and the image is not properly visible.

This kind of basic problems occur in all types of camera and they try to remove the problem by applying some image quality enhancement classifiers for enhancing the visibility of the image and make easy to detecting the person that is coming on the cam range.
In this part separated the literature survey into three modules namely
1) Image Preprocessing
2) Face Detection
3) Sending the Details to the Web gateways
As these are three integral studies which will be helping us to build a secure, Home surveillance system.

2.1 Papers based on Image Preprocessing:
Prashant K. Manglik et al proposed an image preprocessing phase involves numbers of steps and the steps are facing normalization, Grayscale Transformation, and frequency analysis. Transformed images in various angles and they divided the image into two parts first parts contains the eyes and eyebrow, and the second part contains nose, mouth, and cheeks, after that process, the feature vector obtains and they use the feature vector as a training Hopfield Neural Network. 1
Pratiksha Andhare et al proposed finding the x, y coordinate of the object. It converting pixel co-ordinates into real-world coordinates with the help of 2D transformation. The 2D transformation can change the dimension of the image that makes easier to find x, y coordinates of the image. 2
Cheng-Hsiung Hsieh et al one-dimensional (1-D) grey polynomial interpolators (GPIs) for image enlargement are proposed. Note that (i) the randomness inherent in image data affects the performance of polynomial interpolators (PIs) in image enlargement, and (ii) that the preprocessing scheme in grey systems, the first-order accumulated generating operation (1-AGO), is able to reduce randomness in data. In this paper, 1-D grey polynomial interpolators are developed for image enlargement where 1-AGO is used to preprocess image data. To improve the performance of GPIs further, and ? filter is applied to smoothen the interpolated pixels. 3
Hong-Bin Yu et al proposed an image preprocessing schemes, one of them (PQIC) is independent of the image coding algorithm, so they can integrate it into an existing image compression system easily 4
U.K. Jaliya et al proposed a new preprocessing approach to eliminate the radiance effect from the human face images. In our approach they first use Log transform on the input image to improve brightness effect, the output of this is given as input to the Difference-of-Gaussian filters for flattening the image and then performing image normalization so they get the brilliance eliminated image. 5
Dafeng Ren et al proposed a face identification approaches of face image preprocessing before recognition, with brightness- reflection model of homomorphic filtering and image multiplication method of image preprocessing for getting the improvement of human face recognition rate. Homomorphic filtering can decrease low frequency noise in the frequency domain and enhance image details at same time decrease the high frequency noise of the image. By changing the image multiplication coefficient can modify the brilliance of the image. 6
2.2 Papers based on Face Detection:
Kirti Dang et al proposed various face detection algorithms are discussed and analyzed like Viola-Jones, SMQT features & SNOW Classifier, Neural Network-Based Face Detection and Support Vector Machine-Based face detection. All these face detection methods are compared based on the precision and recall value premeditated using a DetEval Software which deals with précised values of the bounding boxes around the faces to give accurate results. 7
Mangayarkarasi Nehru et al proposed a study based approach for detecting human faces using the Viola-Jones algorithm. They train the computer to automatically identify the human faces from the given images irrespective of the brilliance conditions. Based on the experimental results they have discussed the Viola-Jones Cascade Object Detector which uses various filters and the features to detect the various parts of the face. 8
I Gusti Ngurah Made Kris Raya et al proposed that use of face detection system with Viola-Jones method mounted on a surveillance camera in a room as a CCTV is conducted. The camera turns on automatically and moves toward an object corresponding to the input received from a laser range finder. It makes use of the camera becomes more efficient. The output of this research is that the use of the Viola-Jones method can immediately detect a human face more than once, shoot it in real-time, and know the ideal face position to be detected. 9
Mohammad Da’san et al proposed that a multi-stage model for face detection is integrated based on Viola and Jones algorithm, Gabor Filters, Principal Component Analysis, and Artificial Neural Networks (ANN). 10
Ali Sharifara et al proposed that face detection method including feature-based, appearance-based, and knowledge-based and template matching. Also, the study presents the effect of applying Haar-like features along with neural networks. They also conclude this paper with some discussions on how the work can be taken further. 11
Michael Jones et al. revealed that using a machine learning approach for visually objects detections and it performs image processing for rapidly detecting the images and it’s quite faster than other. They use three parameters and they are an Integral image, ADA boost and the third is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on a promising object-like region. 12
Timo Ojala et al. revealed that, the multiresolution grayscale and rotation invariant texture category based on Local binary patterns and prototype distributions. 13
Timo Ahonen et al. revealed that Local binary pattern (LBP) texture feature is an efficient facial image representation by performing the operation on these features vectors. 14
Peter N. Belhumeur et al. revealed that, a face identification algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, they consider each pixel in an image as a coordinate in a high-dimensional space. 15
Ji Zhu et al. revealed that the Adaboost algorithm is used for minimizing exponential loss for the multi-class classification. 16
Rajkiran Gottumukkal et al. revealed that PCA algorithm improved the recognition rate for large variation and lighting directions and facial expression. By dividing the image into parts of sub-images and applied the PCA approach for increasing the accuracy rate. 17
2.3 Papers based on sending the details to the web gateways with the help of IOT:
Nashwan Adnan Othman et al. revealed that they use a camera for getting the image and detecting the face when any action is found through the Passive Infrared sensor. They use Computer vision for detecting the images and grabbed the image and sent the images it to the mobile phones or tablets. 18
Ilhan AYDIN et al revealed that human is detected in the captured image and sends images to a mobile phone by using telegram app. 19
Agung Nugroho Jati et al proposed that detected face is captured by the camera and the final image is sent to the server for further computation through a mobile app. 9
Table 1: Different Image Preprocessing techniques
Sr No Title Authors Algorithm/Methods Result
1 Facial Expression Recognition Prashant K. Manglik Gray Scale Transformation Increases the efficiency with which the features will be tracked
2 Pick and Place Industrial Robot Controller with Computer Vision Pratiksha Andhare Pixels Co-ordinate transformation Visual grasping of objects are using the technique of camera mounted on a robot
3 One-Dimensional Grey Polynomial Interpolators for Image Enlargement Cheng-Hsiung Hsieh Polynomial Interpolation It seems also true
When compared with the 2-D NNI, BLI, and BCI even the proposed GPIs are 1-D PIs.
4 A novel image preprocessing scheme based on face detection Hong-Bin Yu Pre-quantization with importance classification Better subjective quality can be achieved, especially at low bit-rate.

5 An Efficient Illumination Invariant Human Face Recognition using New Preprocessing Approach U.K. Jaliya Log transform, DoG (Difference of Gaussian), HE (Histogram Equalization), AHE (Adaptive Histogram Equalization). Can significantly improve the recognition rates of face images under different lighting conditions compared with existing techniques.

6 A Novel Approach of Low-Light Image Used for Face Recognition
Dafeng Ren Homomorphic filtering They put forward a method based on the intensity of illumination- reflection model of homomorphic filtering and image multiplication of image preprocess, and it’s performance have been verified in the YALE database
Table 2: Different Face Detection Techniques
Sr No Title Author Algorithm/Method Results
1 Review and Comparison of Face Detection Algorithms Kirti Dang Viola-Jones face detector, SMQT Features and SNOW Classifier, Support Vector Machines-Based face detection, Neural Network-Based Face Detection Highest value is of Viola-Jones followed by SMQT Features and SNOW Classifier Method then Neural Network for facial detection and at last is the Support vector Machine. So the best amongst all of these algorithms is Viola-Jones for the face detection
2 Illumination Invariant Face Detection Using Viola Jones Algorithm Mangayarkarasi Nehru Viola-Jones algorithm Time Complexity Reduced
3 Analysis Realization Of Viola-Jones Method For Face Detection On CCTV Camera Based On Embedded System
I Gusti Ngurah Made Kris Raya Viola-Jones Performs is too high capacity and thus it is not optimal for face detection in real time.

4 Mobility Based Routing Protocol with MAC Collision Improvement in Vehicular Ad Hoc Networks Face Detection using Viola and Jones Method and Neural Networks Mohammad Da’san Viola and Jones Shows that the performance was improved as a result of reducing the false positive detected images, the Detection Rate increased from 86.23% to 90.31%
5 Rapid Object Detection using a Boosted Cascade of Simple Features Michael Jones AdaBoost algorithm Insights which are quite generic and may well have broader application in computer vision and image processing
6 Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns Timo Ojala Nonparametric Improved quality
7 Face Description with Local Binary Patterns: Application to Face Recognition Timo Ahonen Component based face recognition The proposed methodology is assessed with the face recognition task. However, a similar method has yielded in outstanding performance in face detection
8 Eigen faces vs. Fisher faces: Recognition Using Class Specific Linear Projection Peter N. Belhumeur Nearest Neighbor Classifier The Fisher face method appears to be the best at simultaneously handling variation in lighting and expression. As expected, the Linear Subspace method suffers when confronted with variation in facial expression
9 Multi-class AdaBoost Abdel-Mehsen Ahmad multi-class classi?cation Performance Improvement in System
10 A improved face recognition Technique based on modular PCA approach Rajkiran Gottumukkal Modular PCA Modular PCA is better than PCA proved
Table 3: Different Method of Sending Detail to clients
No Title Author Application/Webgateways Results
1 A New IoT Combined Face Detection of People by Using Computer Vision for Security Application Nashwan Adnan Othman Telegram Four operations like find motion, grab pictures, find human faces and sending result and notification to the user’s Smartphone
2 A New IoT Combined Body Detection of People by Using Computer Vision for Security Application Ilhan AYDIN Telegram It will alert the client if any person has entered the house or office. The smartphone is the main device of the system that is utilized by the client to obtain notifications with the captured images
3 Analysis Realization Of Viola-Jones Method For Face Detection On CCTV Camera Based On Embedded System Agung Nugroho Jati FTP Servers This system uses ftp for the process of sending the image of face detection from client to server. This experiment aims to find out whether all images from facial detection are sent to the server or not. This experiment is done by trying to do face detection, the process is said to succeed when all the detected image can sent to the server.


Fig 2: Different Face Detection Techniques
We have studied different combination types of combination that are done in the Viola-Jones method and we premeditated that the face detection accuracy varies on the method and the blend that we apply on the Images. As the above figure suggests that Viola-Jones has 86.23%, Viola-Jones + Gabor filters have 90.31%, SVM Based 76.25 and Neural Network has a 96.6% accuracy rate.

Fig 2: Different Image Preprocessing Techniques
We have studied different types of Image Preprocessing techniques, In which we studied that in time of detection the image then in is not compulsory that the image quality / Resolution is clear. Many time image has contained some noise or low or low contrast that effect on the image and the image is not clearly visible, that why they firstly apply image preprocessing techniques for getting the clear image that easily helps to identify the Object / Person.
V. Conclusion:
In this review paper, a face detection and recognition with the security system that they have to design for capture an image and send it to a smartphone in the form of the text message by using web gateway. So, when a face is detected and recognized, the system will notify the user by using a smartphone and displays who is he in that area. By adding the face identification system, people will be easily recognized and a safer city will be built. Also, a possible solution is proposed to utilize computer vision in the IoT in this paper. The smartphone is the main benefit of this paper which is utilized by the client to obtain notifications with the captured images. This system helps to enhance and automate the security of industries, cities, homes, and towns. In this paper, they have to use Face detection algorithm is used to recognize faces. Also, the result of the algorithm is compared with different face detections algorithm. The results show that the better outcome that will be selected for the implementation.

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