Real time Face Detection and Optimal Face Mapping for Online Classes

The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.

A web-based application for face detection in real-time images and videos

Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

Deteksi Suhu Tubuh Dan Masker Otomatis Dengan Metode Haar Casecade Sebagai Solusi Pencegahan Penularan Covid-19

Pada satu tahun terakhir COVID-19 atau yang lebih dikenal dengan wabah virus korona menyebar ke seluruh dunia termasuk juga Indonesia. Gejala paling umum adalah demam dengan suhu tubuh tinggi. Pemerintah sudah memberi aturan agar saat melakukan aktivitas di luar ruangan untuk menerapkan 5M di mana diantaranya menggunakan masker, guna untuk menghambat penyebaran COVID-19. Hal tersebut yang menjadi dasar untuk membuat suatu alat deteksi suhu tubuh dan masker secara otomatis dengan menggunakan Raspberry Pi sebagai mikrokontroler, pengecekan suhu tubuh dengan sensor AMG8833, kamera dengan metode haar cascade untuk mendeteksi masker dan speaker sebagai imbauan pesan suara apabila tidak menggunakan masker dan suhu tinggi > 38°C. Hasil pengujian masker dengan k-fold cross validation didapatkan akurasi 72% dari 100 data, untuk pengujian jarak optimal di jarak 1 meter, perbandingan pengukuran thermogun dengan sensor AMG8833 didapatkan tingkat keberhasilan 75% dari 40 data, untuk pengujian jenis masker dapat mendeteksi hingga tingkat keberhasilan 100% dari 45 data, untuk pengujian aksesoris wajah didapatkan tingkat keberhasilan 75% dari 20 data, untuk pengujian dari berbagai wajah berbeda didapatkan persentase keberhasilan 100% untuk deteksi masker dari 25 data, perbandingan pengukuran thermogun dengan sensor AMG8833 didapatkan eror 0.6%, akurasi 99.5%, untuk pengujian di luar ruangan didapatkan persentase keberhasilan 100% dengan waktu deteksi yang dibutuhkan cukup lama karena tingkat cahaya yang kurang bagus. Rata-rata waktu deteksi yang dibutuhkan dari seluruh data pengujian adalah 2.50 detik.Pada satu tahun terakhir COVID-19 atau yang lebih dikenal dengan wabah virus korona menyebar ke seluruh dunia termasuk juga Indonesia. Gejala paling umum adalah demam dengan suhu tubuh tinggi. Pemerintah sudah memberi aturan agar saat melakukan aktivitas di luar ruangan untuk menerapkan 5M di mana diantaranya menggunakan masker, guna untuk menghambat penyebaran COVID-19. Hal tersebut yang menjadi dasar untuk membuat suatu alat deteksi suhu tubuh dan masker secara otomatis dengan menggunakan Raspberry Pi sebagai mikrokontroler, pengecekan suhu tubuh dengan sensor AMG8833, kamera dengan metode haar cascade untuk mendeteksi masker dan speaker sebagai imbauan pesan suara apabila tidak menggunakan masker dan suhu tinggi > 38°C. Hasil pengujian masker dengan k-fold cross validation didapatkan akurasi 72% dari 100 data, untuk pengujian jarak optimal di jarak 1 meter, perbandingan pengukuran thermogun dengan sensor AMG8833 didapatkan tingkat keberhasilan 75% dari 40 data, untuk pengujian jenis masker dapat mendeteksi hingga tingkat keberhasilan 100% dari 45 data, untuk pengujian aksesoris wajah didapatkan tingkat keberhasilan 75% dari 20 data, untuk pengujian dari berbagai wajah berbeda didapatkan persentase keberhasilan 100% untuk deteksi masker dari 25 data, perbandingan pengukuran thermogun dengan sensor AMG8833 didapatkan eror 0.6%, akurasi 99.5%, untuk pengujian di luar ruangan didapatkan persentase keberhasilan 100% dengan waktu deteksi yang dibutuhkan cukup lama karena tingkat cahaya yang kurang bagus. Rata-rata waktu deteksi yang dibutuhkan dari seluruh data pengujian adalah 2.50 detik.

COMPARISON OF FACE RECOGNITION ALGORITHMS FOR SURVEILLANCE OF CHILDREN USING OPENCV.

Proposing a security system for surveillance of home alone children for safety purpose and send an alert to the register mobile number if some kind of intrusion is detected. I have used Viola-Jones algorithm to detect human face from the live camera and then frame is resized then resized image is processed by the Local Binary Pattern Histograms (LBPH) algorithm and save the model in a YML file and then it is implemented on live cam feed in which the algorithm will detect the face and if some unknown face has been identified it will trigger a notification to the registered mobile number using a python library named [Pywhatkit] so the user can perform security measures. Keywords: Face recognition, Open-CV, HAAR cascade, face recognition.

Implementation of wheelchair controller using mouth and tongue gesture

In this paper, a simple human-machine interface allowing people with severe disabilities to control a motorized wheelchair using mouth and tongue gesture is presented. The development of the proposed system consists of three principal phases: the first phase is mouth detection which performed by using haar cascade to detect the face area and template matching to detect mouth and tongue gestures from the lower face region. The second phase is command extraction; it is carried by determining the mouth and tongue gesture commands according to the detected gesture, the time taken to execute the gestures, and the previous command which is stored in each frame. Finally, the gesture commands are sent to the wheelchair as instruction using the Bluetooth serial port. The hardware used for this project were; laptop with universal serial bus (USB) webcam as a vision-based control unit, Bluetooth module to receive instructions comes from the vision control unit, standard joystick used in case of emergency, joystick emulator which delivers to the control board signals similar to the signals that are usually generated by the standard joystick, and ultrasonic sensors to provide safe navigation. The experimental results showed the success of the proposed control system based on mouth and tongue gestures.

Low Cost System for Face Mask Detection Based Haar Cascade Classifier Method

In December 2019, there was a pandemic caused by a new type of coronavirus, namely SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) spread almost throughout the world. The World Health Organization (WHO) named it COVID-19 (Coronavirus Disease). To minimize the spread of the COVID-19, the Indonesian government announced a policy for the social distancing of 1-2 meters and wearing a medical mask. In this study, a mask detection system was built using the Haar Cascade Classifier method by detecting the facial areas such as the nose and lips. The study aims to distinguish between using masks and on the contrary. It is expected that the mask detection system can be implemented to provide direct warnings to people who do not wear masks in public areas. The results using the Haar Cascade Classifier method show that the system designed is able to detect faces, noses, and lips at a light intensity of 80-140 lux. The face is detected at a distance of 30-120cm, while the nose is at a distance of 30-60cm, while the lips are at a distance of 30-70cm. The system designed can perform the detection process at a speed of 5 fps. The overall test results obtained a success rate of 88,89%.

Secure Online Transaction using Iris

In this project, we are planning to create a strong robust calculation for executing cash in higher level security reason with high acknowledgment rates in a shifting environment. To begin with, Haar cascade based calculation has been connected for quick and basic confront location from the input picture. The confront picture is at that point being changed over into grayscale picture. After that, the iris, eyebrows, nose, mouth of candidates are extricated from the escalated valleys from the recognized confront.

Secure Online Transaction using Iris

In this project, we are planning to create a strong robust calculation for executing cash in higher level security reason with high acknowledgment rates in a shifting environment. To begin with, Haar cascade based calculation has been connected for quick and basic confront location from the input picture. The confront picture is at that point being changed over into grayscale picture. After that, the iris, eyebrows, nose, mouth of candidates are extricated from the escalated valleys from the recognized confront.