Abstract
Automatic object detection is a common phenomenon today. To detect an object first thing is captured, is the image of the object. Now in an image categorically different types of objects are possible. Here, we are considering human face as a most common object. Day by day, the number of application based on face detection is increasing. So the demand of highly accurate and efficient face detection algorithm is on the high. In this paper, our motive is to study different types of face detection techniques and compare them. Various face detection techniques like using Haar-like cascade classifier, Local Binary Pattern cascade classifier and Support Vector Machine-based face detection methods are compared here. All these techniques are compared based on time, accuracy, low light effect, people with black face and with false object and based on memory requirement.
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Mondal, S.K., Mukhopadhyay, I., Dutta, S. (2020). Review and Comparison of Face Detection Techniques. In: Chakraborty, M., Chakrabarti, S., Balas, V. (eds) Proceedings of International Ethical Hacking Conference 2019. eHaCON 2019. Advances in Intelligent Systems and Computing, vol 1065. Springer, Singapore. https://doi.org/10.1007/978-981-15-0361-0_1
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DOI: https://doi.org/10.1007/978-981-15-0361-0_1
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