The system of face detection based on OpenCV

Fan, Xianghua; Zhang, Fuyou; Wang, Haixia; Lu, Xiao

doi:10.1109/ccdc.2012.6242980

Cited by 20 publications

(5 citation statements)

References 10 publications

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“…The relative positions of eyes, nose, and mouth of different people are fixed, and the lip region can be located according to the proportion of the face. Firstly, the face can be detected by the Haar feature and AdaBoost cascade classifier [28]. Puviarasan et al [29] locate lip region according to face width and height.…”

Section: ) Face Structure-based Methodsmentioning

confidence: 99%

A Survey of Research on Lipreading Technology

et al. 2020

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Section: ) Face Structure-based Methodsmentioning

confidence: 99%

A Survey of Research on Lipreading Technology

et al. 2020

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“…These methods allow us to build face models and compare them to the coincidence level of the detection region of a face. Subsequently the possible region of the face is obtained (Fan, Zhang, Wang, & Lu, 2012).…”

Section: Methodsmentioning

confidence: 99%

“…The AdaBoost face detection algorithm is a method used to obtain face regions, which later will allow us to perform face recognition, it includes Harr-like selection features and calculates the features of a typical face through the image integral. Extended Harr-like features can also be used rodigos@uisarel.edu.ec to improve accuracy when detecting a face, which are divided into edge features, linear features, and center-surround features (Fan et al, 2012). A graphic representation of these characteristics can be seen in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…In Figure 2 it shows that the characteristic value composed by II and IV is the variation between the sum of IV pixels and the sum of II pixels. The former is the difference between the sum of integral image value of A and D and the sum of integral image value of B and C. The later is the sum of integral image value of B and integral image value of A (Fan et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

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Face recognition for automatic vehicle ignition based on Raspberry Pi

Nuñez

2020

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In this project a facial recognition application for automatic vehicle ignition is developed. This application is built using a Raspberry Pi as the hardware platform and the OpenCV library for computer vision as the software component. In this research the different methods for automobile security are analyzed, as well as, the different methods used to perform face recognition. The main goal of this application is to enhance the security system of the vehicle, allowing to ignite the vehicle only by register users. To achieve this goal three main processes are carried out, face detection, data gathering, and training the system to grant access through face recognition.

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“…Zeng [7] proposed new facial feature extensions in his paper which consisted of 1) visual-based feature extraction and 2) geo-metric-based rendering, Visual-based extraction removed the important features of a face (eyes, mouth, eyebrows) and geo-metric-based-rendering integrated geometric division based on surface geometric features. Zhang [8] proposed a modified version of the OpenCV, based on the algorithm of AdaBoost. He also used two different methods to get face recognition.…”

Section: Literature Surveymentioning

confidence: 99%

Proposed Music Mapping Algorithm Based on Human Emotions

Burnwal

Mishra

Annapurani

2023

IoT, Cloud and Data Science

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Facial recognition based music system plays an important role in the treatment of human psychology. Face recognition system is an extensively used technique in most of the applications such as security system, video processing, in surveillance system and so on. People are often confused while choosing the kind of music they would want to listen. Relatively, this paper focuses on making an efficient music recommendation system which will recommend a suitable music to make the person feel sooth using Facial Recognition Techniques. This system uses FER-2013 dataset for training of the CNN, which is made using mini-xception architecture. Augmentation techniques are used for increasing the number of images in the dataset for training, which helps to increase the accuracy of the prediction. The face is captured using webcam and facial extraction is done using Haarcascade classifier and then sent to the CNN layers. The mini xception algorithm used in these CNN layers makes the system lighter and efficient as compared to existing systems. The accuracy of the proposed model is calculated and found to have reached the barrier threshold of 95% and average accuracy was found to be 90%. The song is recommended to the user using the proposed mapping algorithm.

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The system of face detection based on OpenCV

Cited by 20 publications

References 10 publications

A Survey of Research on Lipreading Technology

A Survey of Research on Lipreading Technology

Face recognition for automatic vehicle ignition based on Raspberry Pi

Proposed Music Mapping Algorithm Based on Human Emotions

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