Face detection method based on skin color and template matching

Biometric identification has advanced vastly since many decades ago. It became a blooming area for research as biometric technology has been used extensively in areas like robotics, surveillance, security and others. Face technology is more preferable due to its reliability and accuracy. By and large, face detection is the first processing stage that is performed before extending to face identification or tracking. The main challenge in face detection is the sensitiveness of the detection to pose, illumination, background and orientation. Thus, it is crucial to design a face detection system that can accommodate those problems. In this paper, a face detection algorithm is developed and designed in LabVIEW that is flexible to adapt changes in background and different face angle. Skin color detection method blending with edge and circle detection is used to improve the accuracy of face detected. The overall system designed in LabVIEW was tested in real time and it achieves accuracy about 97%.

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A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

Traitement du signal ◽

10.18280/ts.370606 ◽

2020 ◽

Vol 37 (6) ◽

pp. 929-937

Author(s):

Xiaoying Yang ◽

Nannan Liang ◽

Wei Zhou ◽

Hongmei Lu

Keyword(s):

Face Detection ◽

Skin Color ◽

Detection Rate ◽

Detection Method ◽

Random Search ◽

Color Space ◽

Color Model ◽

High Detection Rate ◽

Adaboost Algorithm ◽

Skin Color Model

This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

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Face Detection in a Mixed-Subject Document

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i6.pp2828-2835 ◽

2016 ◽

Vol 6 (6) ◽

pp. 2828

Author(s):

Lhoussaine Bouhou ◽

Rachid El Ayachi ◽

Mohamed Baslam ◽

Mohamed Oukessou

Keyword(s):

Face Detection ◽

Skin Color ◽

Template Matching ◽

Detection System ◽

Second Phase ◽

Text And Image ◽

Important Research Topic ◽

The Face ◽

Second Category ◽

Text Images

<p>Before you recognize anyone, it is essential to identify various characteristics variations from one person to another. among of this characteristics, we have those relating to the face. Nowadays the detection of skin regions in an image has become an important research topic for the location of a face in the image. In this research study, unlike previous research studies related to this topic which have focused on images inputs data faces, we are more interested to the fields face detection in mixed-subject documents (text + images). The face detection system developed is based on the hybrid method to distinguish two categories of objects from the mixed document. The first category is all that is text or images containing figures having no skin color, and the second category is any figure with the same color as the skin. In the second phase the detection system is based on Template Matching method to distinguish among the figures of the second category only those that contain faces to detect them. To validate this study, the system developed is tested on the various documents which including text and image.</p>

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