Authors: Salila Hegde, Nandini M S, Rahila Samar, Ramyashri H N
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In this paper face recognition system for the purpose of security is explored whose design is based on Raspberry Pi system. An add on temperature detection system also gives detection of COVID affected people. With the COVID-19 pandemic, temperature checking before you enter a premise is a necessary step to ensure you are well and help keep others safe. One of the most common ways is to use a handheld digital thermometer to find out your own temperature and recording it on some kind of logbook placed near the front door. For a workplace, employees are encouraged to record their temperature twice a day.
Face recognition systems are capable of detecting faces from nearby and far off places. This is achieved mainly by two ways; one is through sophisticated camera and other is through trained ML algorithms which can process the input image from camera and recognize faces in real time. Many features such as gender recognition, age detection, and live face detection can be included to train algorithms-1-7].
Yang etal.  proposed face detection based on human knowledge and set of rules. The approach is feature invariant, which locates the face by extraction of structural features. A trained classifier is used to discriminate non facial and facial regions.
Turk and Pentland  proposed a face detection method wherein the dimensionality of face model is decreased from size of pixel to principal basis.
This principal basis contains sufficient information in the encoded form. This methodology best preserves the data in embedding space, but suffers in discriminating capability.
Belhumeur and coauthors propose face detection by dimensionality reduction based on linear discriminant analysis (LDA) . Their method improved face discrimination but performs poor in face verification.
Dominik Schiller and coauthors developed a facial expression recognition model that makes use of saliency maps . The non-relevant information is hidden while transferring knowledge from an arbitrary source to destined network. This method is independent of model employed because the experience is solely transferred via input data augmentation.
Masi et al. propose presented methods to improve realistic datasets with critical facial types . Faces in the list of datasets are controlled along with convolutional neural systems to coordinate inquiry pictures
In the recent times of COVID, the feature to recognize masked and unmasked faces also been developed. Our goal through this project is to explore the feasibility of face recognition system implementation on Raspberry Pi based system and detect if that person belongs to the same institution. Display his/her present status and then followed by auto detection of body temperature of human body for COVID-19 detection.
This paper explores the face recognition system using Raspberry Pi based system and detects if that person belongs to the same institution/organization.
To display his/her present status and then followed by auto detection of body temperature of human body. High quality camera is useful in capturing clear images of object. Low lighting can cause hinder in getting a good image input.
The algorithms trained by images should be from different angles, with makeup and without makeup, faces with spectacles so that two different faces of same person should not confuse the algorithm and produce errors. This might cause huge problems when it comes to recognition for criminal offenses cases and defence related security cases. Liveness of input feed can be manipulated using makeup which can fool the system to grant access.
Figure 1 depicts the block diagram of the proposed method for face detection and temperature sensing for COVID-19 detection.
The user application will have following features:
A. Design Implementation
2. Where gx,gy,g and θ are horizontal and vertical gradients, magnitude and direction of gradient respectively
3. Image is divided into 8×8 segments and a histogram of gradients is found for each of these 8×8 segments. An 8×8 color image segment
4. Has 8x8x3 = 192 values. The gradient of this segment has two values (magnitude and direction) per pixel, hence total 8x8x2 = 128 numbers.
5. These 128 values are represented using a 9-bin histogram which is stored as an array of nine numbers. Histogram of these 128 numbers is a vector of 9 bins which correspond to angles o 0, 20…160.
6. Based on the direction a bin is selected and based on the magnitude a vote is selected.
7. Pixel which is encircled in Blue color in figure 3 has magnitude of 2 and an angle of 80 degrees. Hence it adds two to the histogram bin. Pixel which is encircled in Red has magnitude of 4 and angle of 10 degrees. The vote for this pixel is split between the two bins.
8. All the pixels in the 8×8 segments contribute to make 9 bin histogram as shown in figure 4.
B. Face Detection Algorithm
Viola-Jones Face Algorithm uses boosting technique which is machine learning algorithm. In this feature selection is done by each feature evaluation using its value. Viola Jones classifier is also called as Haar cascade classifier which has following 4 stages;
2. Creation of Integral image
An integral image helps significantly to reduce time and power to process large number of features in an image in real time.
4. Cascade classifiers
III. FLOW CHART
5. Steps of flow
Flow chart is continued in next page also.
IV. FACE RECOGNITION RESULTS
The face recognition and temperature detection system as proposed in our project will result as a very useful system in our society and meet the expectations ensuring the safety and control during this pandemic.
Live face detection and recognition results are as follows:
Firstly we implemented facial recognition on the database/ reference images as show below in figures 10 to 15. for three separate images namely RAHILA,FIZA and RAMYA who were our students and staff.
V. TEMPERATURE SENSING
After the face is detected the system follows auto temperature detection process Due to COVID 19 impact, the number of users allowed in a particular room in offices, shops, etc. was restricted. The face detection algorithm allows only a predetermined person to the room. The entry of the person in the restricted room is detected with the help of laser diode and a receiver as shown in figure 16. Once the entry is detected the temperature of person is sensed. If the temperature is less than the preset threshold the person is allowed to enter, otherwise denied.
Infrared Thermometer Sensor - MLX90614 is used for sensing the temperature of the body [13-14].
The MLX90614 has two types of output. The PWM output is 10 bit and has resolution of 0.14°C, while the output provides a resolution of 0.02°C. It has following features. This device comes in an industry standard TO-39 package. We're carrying the 3V version of this sensor.
4. digital interface which is SMBus compatible
5. 10-bit PWM output for continuous reading.
6. High accuracy of 0.5°Cover wide temperature range
7. Measurement resolution of 0.02°C
8. Can be adapted to 8 to 16V applications
9. Works in power saving mode
We are using 3V version of this sensor.
VI. TEMPERATURE SENSING RESULTS
A large number of people entry can be evaluated as The non-contact temperature sensor can make quick measure and display. The use of non-contact temperature sensing devices is a boon to reduce the risk of COVID-19 spread and infections and requirement of cleaning is also minimal. The test result sample images are as shown in figures 18 to 22.
VII. SCOPE AND FUTURE WORK
The scope of this project is promising. Face detection and recognition will prove to be a boon to many high profile organizations like banks, educational institutions and other big companies wherein the security of confidential documents is highly required and entry of any unauthorized person into the organization could lead to theft, and other major security issues which would affect the whole and soul of the company and hence, allowing only the authorized and recognized people inside the building is very much necessary and this project does exactly that. It ensures the security and helps in maintaining the confidentiality of the organization.
This paper proposes an implementation of face recognition and temperature detection system. During capturing and training phase several positive and negative images are created. The design is implemented in Analog and Digital Labs, EC Department, NIEIT, Mysore. Test image samples of students and staff are captured during training phase. “FACE DETECTED” or “FACE NOT DETECTED “status is obtained on terminal during test phase. After that temperature is sensed and Access is tested. This is a low cost device and useful in COVID-19 like situations at Colleges, Universities, Hospitals etc.
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Copyright © 2023 Salila Hegde, Nandini M S, Rahila Samar, Ramyashri H N. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.