Real time face detection robot

Maneesha, K; Shree, Neha; Pranav, Datta R; Sindhu, S.; Gururaj, C.

doi:10.1109/rteict.2017.8256558

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…( 2018) and Mery, D., Mackenney, I., & Villalobos, E. (2019). In PCA-based face recognition, OpenCV is used in the attendance system for marking attendance (Maneesha et al, 2017). An attendance system based on the Haar cascade and local binary pattern histogram algorithm is developed Setumin et al (2019) and Jahan et al (2020).…”

Section: Suggested Citationmentioning

confidence: 99%

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

Sanap,

Narwade,

Goge

et al. 2023

ejtas

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In the 21st century, modern technology is playing an important role in providing innovative on traditional challenges across various domains or sectors. One such challenging task is of daily attendance marking and tracking. Manual attendance requires efforts and it is time-consuming. Sometimes attendance cannot be mark due to human errors. Relying on voice, iris, or fingerprint recognition, increases the complexity and the hardware infrastructure of the system and also increases the cost. To effectively address such issues, we have developed a “Camera based Attendance System”. This system encompasses several crucial stages, including data entry, dataset of multiple people. It is an image-based face recognition system for marking attendance on the SQL database. It excels in detecting and recognizing multiple individuals faces from image and comparing it with the dataset for accurately marking the attendance. This makes the attendance marking process fully automatic. Remarkably, our proposed system attains an impressive recognition and provides the accuracy of approximately 95%. With this solution, daily attendance marking and recording becomes effortless and the stored attendance record can be also used in future if require, eliminating the risk of attendance not getting marked due to human error.

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Section: Suggested Citationmentioning

confidence: 99%

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

Sanap,

Narwade,

Goge

et al. 2023

ejtas

View full text Add to dashboard Cite

show abstract

“…Real-time face identification and tracking systems have been discussed, put into practice, and used in a variety of domains [6] through [10]. The face tracking objective idea can later be extended to smart equipment like robots in order to pursue moving face targets and look for lost face targets [7], [10]. Based on object movement registration techniques like optical flow, human face tracking is then enhanced by Kanade, Lucas, and Tomasi (KLT algorithm).…”

Section: Related Workmentioning

confidence: 99%

Real-time Face Tracking for Service-Robot

Abdullah¹,

Alnuaimy²

2022

Technium

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Real-time human face tracking is suggested in this paper for use while interacting with robots. The system consists of two main parts, the first works to discover the human face, determine its location in relation to the original image, and find the dimensions of the face to be used in the second part of this system. The second part receives the location and dimension of the face and tracks it by controlling the movement of the camera according to the offset between the interval. In order to detect human faces for the earlier job, the Haar cascade method is used, whereas the Kanade-Lucas-Tomasi (KLT) algorithm is used for face tracking under various circumstances. As a trace output from the prior stage, the camera is offset by its offset between image frames. The findings of the experiments demonstrate that real-time tracking of human faces was successful even when the participants were donning glasses, hats, or face-side positions. At a maximum frame rate of 26 fps, experiments were conducted.

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“…The illumination level determined the amount of lighting needed to maintain the contrast in the overall view of objects in the shop window under external natural illumination. [6] introduced the use of Kinect depth cameras in order to detect and track human person and to estimate the human activity.…”

Section: Prelated Workmentioning

confidence: 99%

Development and Validation of a Camera-Based Illumination Controller for People Detection, Tracking and Recognition using Computational Intelligence

Llorente¹,

Dadios²

2019

IJRTE

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Illumination is crucial in human activities and in machine vision applications. For indoor surveillance applications, Infrared (IR) Light Emitting Diodes (LEDs) are the common means of providing illumination to the camera to cause no discomfort to human occupants. While IR provides non-obtrusive illumination for the camera, the same energy consumed does not provide the illumination to indoor spaces of the building. This is important if the premises where the camera is installed is not connected to the main power source or electric grid but derives energy from renewable sources. In this work, an illumination controller based on fuzzy logic system is developed and integrated to a vision system and an LED lighting system to provide a constant level of illumination to an object regardless of its distance from the image sensor. The computer vision system performs human object detection and face recognition and outputs fuzzy values representing the inferred distance of detected objects where the fuzzy system generates crisp output of duty cycle settings for the PWM controller for the LED lighting system to provide the required illumination needed by the vision system. Optimum illumination level for the vision system to perform the detection, tracking and face recognition operations must be provided by the system. Using visible Light Emitting Diodes as source of illumination, the system provides illumination both for the proper operation of the camera and human personnel monitoring the premises where the system is installed. This feature is significant in energy-constrained surveillance applications or where there is no power source derived from the electric grid.

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Real time face detection robot

Cited by 6 publications

References 7 publications

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

Real-time Face Tracking for Service-Robot

Development and Validation of a Camera-Based Illumination Controller for People Detection, Tracking and Recognition using Computational Intelligence

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