Fuzzy Classification, Image Segmentation and Shape Analysis for Human Face Detection

Hmid, M.; Jemâa, Yousra Ben

doi:10.1109/icosp.2006.345953

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Among different operators, the most powerful edge-detection method is the Canny method. It differs from the other ones because it uses two different thresholds allowing it to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges [7]. After edge detection, the first step which decides whether the region represents a face or not is ellipse detection.…”

Section: Face Detectionmentioning

confidence: 99%

“…Due to the diversity of human skin color and for many other reasons such as luminance, noise, and shade, this research proposes a fuzzy approach for a good pixel classification, since fuzzy set theory can represent and manipulate uncertainty and ambiguity. This research uses TakagiSugeno fuzzy inference system (FIS) [7]. The most suitable arrangements that we found for all input images in database are:…”

Section: A Skin Color Segmentationmentioning

confidence: 99%

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Facial Features Approximation for Expression Detection in Human-Robot Interface

Khan

Bhuiyan

2010

2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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This paper presents a facial expression recognition system employing B´zier curves approximation technique. The system is based on facial features extraction using the knowledge of the face geometry and approximated by 3 order B´zier curves representing the relationship between the motion of features and changes of expressions. For face detection, color segmentation based on the novel idea of fuzzy classification has been employed that manipulates ambiguity in colors. Experimental results demonstrate that this method can recognize the facial expressions with an accuracy of more than 90% in all cases. Finally the system has been implemented using a manipulator robot and issuing facial expression commands.

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Section: Face Detectionmentioning

confidence: 99%

Section: A Skin Color Segmentationmentioning

confidence: 99%

Facial Features Approximation for Expression Detection in Human-Robot Interface

Khan

Bhuiyan

2010

2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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“…One promising approach for recognizing up to facial expressions intensities is to consider whole facial image as single pattern [4]. Kimura and his colleagues have reported a method to construct emotional space using 2D elastic net model and K-L expansions for real images [7]. Their model is user independent and gives some unsuccessful results for unknown persons.…”

Section: Literature Reviewmentioning

confidence: 99%

Neuro Fuzzy Model for Human Face Expression Recognition

Burange¹

2012

IOSRJCE

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“…Then, a fuzzy classifier to detect the presence of faces in small windows, with an HSV color model to detect skin, was presented in [33]. Finally, fuzzy representations of YCbCr for modeling skin can be found in [34,35].…”

Section: Fuzzy Sets and Skin Detectionmentioning

confidence: 99%

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Pujol

Jimeno-Morenilla

López

2017

Entropy

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Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.

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Fuzzy Classification, Image Segmentation and Shape Analysis for Human Face Detection

Cited by 8 publications

References 4 publications

Facial Features Approximation for Expression Detection in Human-Robot Interface

Facial Features Approximation for Expression Detection in Human-Robot Interface

Neuro Fuzzy Model for Human Face Expression Recognition

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

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