Door automation through face Recognition

Project Title

Door automation through face Recognition

Project Area of Specialization Artificial IntelligenceProject Summary

• In today’s world of connectivity and smart devices there is an urgent need to modify our existing day to day objects and make them smart, also it is not the era when we can blindly trust the old and conventional security measures, specifically speaking is our door locks. To change and modernize any object we need to eliminate its existing drawbacks and add extra functionality.Face detection is more challenging because of some unstable characteristics, for example, glasses and beard will impact the detecting effectiveness. Moreover, different kinds and angles of lighting will make detecting face generate uneven brightness on the face, which will have an influence on the detection process
• The face recognition procedure simply requires any device that has digital photographic technology to generate and obtain the images and data necessary to create and record the biometric facial pattern of the person that needs to be identified.
• In this project, a smart home security system by using PCA  face detection algorithm is proposed to enhance the security level of the entry system. Face recognition is an interesting but challenging in machine learning field and impacts important applications in many areas such as remote sensing, machine/robot vision, pattern recognition, medical field, banking and security system access, and authentication in personal electronics gadget. In this research paper, we proposed the door lock security system using image processing instead of the traditional key and digital lock system. The image processing mainly consists of three parts, namely face representation, feature extraction and identification of face. Face representation represents how to model a face with PCA algorithms of detection and recognition. The important and distinctive features of the face are extracted in the feature extraction phase. In the identification of the face, the new face image is compared with the images which are already extracted and saved on the database. Face detection and recognition methods were applied to allow the authorized dwellers and the guest and prevent the unwanted person to enter inside the house.
• Face detection is the process of detecting the region of face in an image..If a face is recognized, it is known, else it is unknown. The door will open automatically for the known person due to the command of the microcontroller. On the other hand, alarm will ring for the unknown person. Since PCA reduces the dimensions of face images without losing important features, facial images for many persons can be stored in the database. Although many training images are used, computational efficiency cannot be decreased significantly. Therefore, face recognition using PCA can be more useful for door security system than other face recognition schemes.

Project Objectives

• Unlike other identification solutions such as passwords, verification by email, selfies and images or fingerprint identification, Biometric facial recognition uses unique mathematical and dynamic patterns that make this system one of the safest and most effective ones.
• The objective of face recognition is, from the incoming image, to find a series of data of the same face in a set of training images in a database. The great difficulty is ensuring that this process is carried out in real-time, something that is not available to all biometric facial recognition software providers.
• The facial recognition process can perform two variants depending on when it is performed:
• The one in which, for the first time, a facial recognition system addresses a face to register it and associate it with an identity, in such a way that it is recorded in the system. This process is also known as Digital Onbordingwith facial recognition.
• The variant in which the user is authenticated prior to being registered. In this process, the incoming data from the camera is crossed with the existing data in the database. If the face matches an already registered identity, the user is granted access to the system with his credentials.
• Project is to develop a security management application supported by face recognition. The haar-like cascade features is employed for face detection .  Face recognition has conjointly proved helpful in different transmission science areas. Identity verification analyze the characteristic offacepictures inputfrom a digital video camera or on-line face capturing. Currently we want to maintain security in each organization and each individual needs to enhance their security system. Most of the individuals would like higher security system which supplies complete security solution

Project Implementation Method

• In the beginning of the 1970's, face recognition was treated as a 2D pattern recognition problem.The distances between important points where used to recognize known faces, e.g. measuring the distance between the eyes or other important points or measuring different angles of facial components. But it is necessary that the face recognition systems to be fully automatic. Face recognition is such a challenging yet interesting problem that it has attracted researchers who have different backgrounds: psychology, pattern recognition, neural networks, computer vision, and computer graphics. The following methods are used to face recognition

1. Holistic Matching Methods
2. Feature-based (structural)
3. MethodsHybrid Method

Moreover, technology systems can sometimes vary when it comes to facial recognition, but the general functioning is as follows.

• Face Detection

To begin, the camera will detect and recognize a face, either alone or in a crowd. The face is best detected when the person is looking directly at the camera. The technological advancements have enabled slight variations from this to work as well.

• Face Analysis

Next, a photo of the face is captured and analyzed. Most facial recognition relies on 2D images rather than 3D because it can more conveniently match a 2D photo with public photos or those in a database. Distinguishable landmarks or nodal points make up each face. Each human face has 80 nodal points. Facial recognition software will analyze the nodal points such as the distance between your eyes or the shape of your cheekbones.

• Converting An Image to Data

The analysis of your face is then turned into a mathematical formula. These facial features become numbers in a code. This numerical code is called a faceprint. Similar to the unique structure of a thumbprint, each person has their own faceprint.

•  Finding a Match

Your code is then compared against a database of other faceprints. This database has photos with identification that can be compared.

According to records FBI has access to more than 641 million photos, including 21 state databases such as DMVs. Another example of a database that many have access to is Facebook’s photos. Any photos that are tagged with a person’s name become part of the Facebook database.

The technology then identifies a match for your exact features in the provided database. It returns with the match and attached information such as name and address.
 

Benefits of the Project

Aside from unlocking your smartphone, facial recognition brings other benefits to the companies.

• Enhanced security

The first thing to start with is surveillance. With the help of facial recognition, it will be easier to track down any burglars, thieves, or other trespassers.

On the governmental level, facial recognition can help identify terrorists or any other criminals with the help of the face scan only. The additional bonus is the fact that one cannot hack the technology: there is nothing to steal or change, like in case of a password, for example.

•  Faster processing

The process of recognizing a face takes a second or less — and this is incredibly beneficial for the companies.

In the era of constant cyber attacks and advanced hacking tools, companies need a technology that would be both secure and fast. Considering that facial recognition is almost instant, it grants a quick and efficient verification of a person. In addition, it’s hard to fool this technology so this is another bonus.

•  Automation of identification

Before, security guards had to perform manual identification of a person that took too much time and did not boast high accuracy. But today, facial recognition is completely independent in the identification process and not only takes seconds but is also incredibly accurate.

The 3D facial recognition technology and the use of infrared cameras significantly boosted the level of accuracy of facial recognition and made it really hard to fool.

• Breach of privacy

With the help of this technology, the government can track down the criminals. But at the same time, it can actually track down people like you anytime, anywhere.

So even though facial recognition indeed brings benefits, there is still an awful lot of work to be done before the technology is 100% used fairly and in accordance with human rights for privacy.

•  Massive data storage

Artificial intelligence technology requires massive data sets to “learn” in order to deliver accurate results. And such data sets require a powerful data storage.

So if you are a small or medium-sized company you simply may not have the necessary resources to store all the data. And that might be a problem.

•  Conclusion

Facial recognition is a powerful technology but it has to be used wisely. On one hand, it brings immense advantage to the companies and end-users, helps them enhance their security and track down the trespassers.

• In our opinion, it will take at least 5 years for facial recognition to come in complete correspondence with human rights and one’s privacy. Until then, all we can do is wait and hope for the industry giants
   

Technical Details of Final Deliverable

The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. This can be called eigenspace projection.

The image is a combination of pixels in rows placed one after another to form one single image each pixel value represents the intensity value of the image, so if you have multiple images we can form a matrix considering a row of pixels as a vector

The goal of PCA is to identify patterns in a data set, and then distill the variables down to their most important features so that the data is simplified without losing important traits. PCA asks if all the dimensions of a data set spark joy and then gives the user the option to eliminate ones that do not.

Principal Component Analysis (PCA) provides enhanced accuracy in features based image identification and classification as compared to other techniques. PCA is a feature based classification technique that is characteristically used for image recognition.

With three dimensions, PCA is more useful, because it's hard to see through a cloud of data. In the example below, the original data are plotted in 3D, but you can project the data into 2D through a transformation no different than finding a camera angle: rotate the axes to find the best angle. To see the "official" PCA transformation, click the "Show PCA" button. The PCA transformation ensures that the horizontal axis PC1 has the most variation, the vertical axis PC2 the second-most, and a third axis PC3 the least. Obviously, PC3 is the one we drop.

Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther Industries Education , Medical , Manufacturing , Transportation , Media , Security Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT), Wearables and Implantables, Others, Big DataSustainable Development Goals Industry, Innovation and Infrastructure, Partnerships to achieve the GoalRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	43100
IPC-A22EP-A Camera	Equipment	1	6000	6000
FTDI Board	Equipment	1	3401	3401
Relay Module	Equipment	1	900	900
Solenoid Lock 12V	Equipment	2	1500	3000
Raspberry pi 4 Chip	Equipment	1	24999	24999
Jumper Cables	Miscellaneous	3	1600	4800