Ms. Dorsa Kiaei Profile

Dorsa Kiaei

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Proceedings Article | 27 November 2019 Paper

Human face detection based on skin color with principal component analysis (PCA) algorithm

Dorsa Kiaei, Saeed Tavakoli

Proceedings Volume 11321, 113210G (2019) https://doi.org/10.1117/12.2540481

KEYWORDS: Facial recognition systems, Skin, Principal component analysis, Databases, Detection and tracking algorithms, Image segmentation, Image enhancement, Statistical modeling, Statistical analysis, Image processing

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Identifying and identifying a person's face is one of the challenging issues in computer applications. The main part is the facial recognition of the whole image. Problems include sensitivity to the exposure conditions of the input image. Here is a way to detect human faces in color images based on the edge and color of the skin of the color image by setting the appropriate thresholds. First, image enhancement is performed, especially if the image is obtained from an infinite lighting condition. Then the skin was digested in the laboratory environment. The edges of the image are combined with the tone of the skin color to separate all areas other than the face. For this purpose, skin color detection methods have been used to detect faces. The Eigenface method is also known as the PCA method. This process has been tested in pictures and samples for various samples and has yielded good results. Due to the increasing instances of identity theft and the occurrence of terrorism in the past years, recognizing and identifying a person can determine the unity of a human being with another person's face. For facial recognition of an online monitoring system or an offline image, the main component to be identified is the skin areas. The skin color has been proven to be a powerful and useful indication for face detection, placement, and follow-up. Face detection is simply a facial image for determining the input given, regardless of its size, position, and background. The current evolution of computer technology has increased in this era.

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