Partial face recognition by template matching

Naveena, M; Hemanthakumar, G.; Prakasha, M; Nagabhushan, P.

doi:10.1109/erect.2015.7499034

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…It creates a transmission map that depicts the level of haze in the image using these variables. The image is then made clearer by using the transmission map to eliminate haze from it [13,14]. Image dehazing, underwater photography, and remote sensing are just a few of the areas where dark channel prior has been proven to be successful.…”

Section: Dark Channel Priormentioning

confidence: 99%

Enhancing Safety and Security: Face Tracking and Detection in Dehazed Video Frames Using KLT and Viola-Jones Algorithms

Gurrala,

Talasila,

Madhuri

et al. 2023

IJSSE

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In the context of safety and security, the ability to track and identify faces in hazy conditions presents a significant challenge. The deleterious effects of haze on video quality, such as the diminution of detail, reduction in contrast, distortion of color, and complications in depth estimation, impede effective facial recognition. Additionally, the complexity of live video tracking is exacerbated by factors such as occlusion, positional variations, and lighting changes. Despite these challenges, video sequences offer an abundance of information, surpassing static images in terms of potential data extraction. In this study, a dual approach strategy is employed to detect and track faces in hazy conditions. The Kanade-Lucas-Tomasi (KLT) algorithm, celebrated for its adept feature tracking capabilities, is deployed to execute face tracking. The effectiveness of this algorithm lies in its ability to accurately trace points across successive image frames, a crucial aspect of reliable face tracking. Concurrently, the Viola-Jones algorithm is utilized for face detection. The algorithm harnesses Haar-like features to efficiently discern faces in real-time, effectively overcoming the challenge of identifying faces within video frames. To further enhance the quality of the video, the dark channel prior (DCP) image dehazing technique is employed. This technique improves visibility by increasing contrast and color saturation, whilst concurrently identifying and eliminating air haze from the video frames.

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Section: Dark Channel Priormentioning

confidence: 99%

Enhancing Safety and Security: Face Tracking and Detection in Dehazed Video Frames Using KLT and Viola-Jones Algorithms

Gurrala,

Talasila,

Madhuri

et al. 2023

IJSSE

View full text Add to dashboard Cite

show abstract

“…Then again, the use of a profound learning network is more difficult on the grounds that the amount of preparing information isn't adequate to prepare the profound learning networks [8] for this application which powers to utilization of move learning. Lastly, this check cycle is solidified to perceive upand-comers face by playing out the arrangement task inside a brought together Support Vector Machine (SVM) [9][10][11][12].…”

Section: Literature Surveymentioning

confidence: 99%

Person Identification Using Face and Speech Recognition for Visually Challenged with Mask Detection

Anitha

Manoj

et al. 2021

Advances in Parallel Computing Technologies and Applications

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A system and method for facilitating a visually impaired person for identifying a person. The method includes the step of storing a plurality of instructions for facilitating the visually impaired person identify the person in front of them by their face and/or voice characteristics by updating our project with Mask Detection using OpenCv and keras. It includes the step of receiving voice signals from the person present in surrounding of the visually impaired person and includes the step of capturing the pictures of a particular person and their surroundings of the visually impaired person and storing the processed data into the database or any storage devices. The data will be processed to AWS server or any local storage for processing and determining the person with the help of the database we already have. After processing and identifying the person with the help of face and voice recognition modules the name is sent to the visually impaired user’s phone in the form of a text message which will be read aloud by his phone’s virtual assistant.

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