Fuzzy classification, image segmentation and shape analysis for Human face detection

Hmida, M. Ben; Jemâa, Yousra Ben

doi:10.1109/icecs.2006.379870

Cited by 7 publications

(3 citation statements)

References 4 publications

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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%

Untitled

2012

IOSRJCE

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This paper present an approach to recognize human face expression and emotions based on some fuzzy pattern rules. Facial features for this specially eye and lips are extracted an approximated into curves which represents the relationship between the motion of features and change of expression. This paper focuses the concepts like face detections, skin color segmentation, face features extractions and approximation and fuzzy rules formation. Conclusion based on fuzzy patterns never been accurate but still our intension is to put more accurate results.

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Section: Literature Reviewmentioning

confidence: 99%

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2012

IOSRJCE

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“…Each input has three sub-sets: light, medium and dark. Our algorithm uses the concept of fuzzy logic IF-THEN rules; these rules are applied in each pixel in the image in order to decide whether the pixel represents a skin or non-skin region [21]. The fuzzy logic rules applied for skin detection are the following: The first step is to determine, for each input, the degree of membership to the appropriate fuzzy sets via membership functions.…”

Section: A Preprocessing and Skin Color Extractionmentioning

confidence: 99%

Automatic Gabor Features Extraction for Face Recognition using Neural Networks

Jemâa

Khanfir

2008

2008 First Workshops on Image Processing Theory, Tools and Applications

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We present in this paper a biometric system of face detection and recognition in color images. The face detection technique is based on skin color information and fuzzy classification. A new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their correspondent geometrical points. These fiducial points are described by sets of wavelet components which are used for recognition. To achieve the face recognition, we use neural networks and we study its performances for different inputs. We compare the two types of features used for recognition: geometric distances and Gabor coefficients which can be used either independently or jointly. This comparison shows that Gabor coefficients are more powerful than geometric distances. We show with experimental results how the importance recognition ratio makes our system an effective tool for automatic face detection and recognition.

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“…Visión artificial Detección de rostros [Albiol et al, 2001], [Hmid & Jemaa, 2006], [Xia et al, 2006] Reconocimiento de escritura [Fujisawa et al, 1992], [Kumar & Singh, 2010], [Sheshadri et al, 2010] Reconocimiento de matrículas de automóvil [Anagnostopoulos et al, 2008], [Kaushik et al, 2009],…”

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Aplicación de técnicas de inteligencia artificial para contribuir en la detección de microcalcificaciones en mamografía digitalizada

Domínguez¹

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Fuzzy classification, image segmentation and shape analysis for Human face detection

Cited by 7 publications

References 4 publications

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Automatic Gabor Features Extraction for Face Recognition using Neural Networks

Aplicación de técnicas de inteligencia artificial para contribuir en la detección de microcalcificaciones en mamografía digitalizada

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