Object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier

Mohan, K.; Chandrasekhar, P.; Ramanaiah, K. Venkata

doi:10.1504/ijcaet.2020.106213

Cited by 3 publications

(2 citation statements)

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“…In the case of still images, it is important to adapt the detection algorithm to a wider range of image types. In the case of dynamic video sequences, the speed of detection is fundamental [10]. Face detection can be divided into colour images and greyscale images, depending on the classification of the colour information.…”

Section: Detection Of Face Imagesmentioning

confidence: 99%

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Face Detection Based on Cascading Support Vector Machines

2022

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With the development of society, the world's population mobility, mobility speed and mobility area are increasing, and face detection and recognition technology also plays an important role in the management and statistics of mobile population, and has great application prospects in digital entertainment. The aim of this paper is to study face detection based on cascaded support vector machines. The performance metrics specified in the training are reduced, effectively simplifying the structure of the cascading classifier, which is then used as a filter, followed by the introduction of a nonlinear support vector machine main classifier based on rectangular features. Experimental results show that although the detection speed does not reach the real-time level of Viola's method, it is still a significant improvement over the pixel-based method, along with a higher face detection rate.

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Section: Detection Of Face Imagesmentioning

confidence: 99%

“…In recent years, the study of algorithms related to face detection and their implementation has started to attract the attention of many scholars as an independent research topic [5][6]. Slim Ben Chaabane proposed a histogram (HOG) based and support vector machine directional gradient algorithm for human eye localization.…”

Section: Introductionmentioning

confidence: 99%