Segmentation Algorithm for Multiple Face Detection for Color Images with Skin Tone Regions

Lakshmi, H.C. Vijay; PatilKulakarni, S.

doi:10.1109/icsap.2010.42

Cited by 21 publications

(16 citation statements)

References 4 publications

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“…The number of false acceptances was higher in skin tone regions and there were false rejections in skin tone images with complex background. In [12] the number of false acceptances and false rejections were reduced considerably when the edge strength of the second level approximation was used as the feature set.…”

Section: A Training Phasementioning

confidence: 99%

“…Step-1: Skin segment the input color image using the algorithm proposed in [12]. For skin segmented regions with size larger than the window size considered, use sliding window technique proposed in [12].…”

Section: A Training Phasementioning

confidence: 99%

“…For skin segmented regions with size larger than the window size considered, use sliding window technique proposed in [12].…”

Section: A Training Phasementioning

confidence: 99%

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Face Detection in Skin-Toned Images Through Wavelet Edges and Neural Network

Lakshmi¹,

Kulkarni²

2012

IJCEE

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Abstract-In this paper, an improved algorithm to detect faces in images with skin tone regions is proposed. Among the segmented candidate regions, facial edges detected using Canny and Sobel operators on wavelet approximation are suggested as feature set. These features are further classified using a neural-net proposed. Results of the test using the proposed algorithm are compared with those of the previous ones.

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Section: A Training Phasementioning

confidence: 99%

Section: A Training Phasementioning

confidence: 99%

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Face Detection in Skin-Toned Images Through Wavelet Edges and Neural Network

Lakshmi¹,

Kulkarni²

2012

IJCEE

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“…Understanding the behavior of individual and group, social networking methods assuage the analysts to revealing hidden patterns and knowledge from social communication [28,35]. This discovered knowledge can be useful in law enforcement, homeland security, organizations, and closed world environment.…”

Section: Introductionmentioning

confidence: 99%

Link Mining: A Computer Vision and Pattern Mining Approach

Mishra¹,

Nandi²

2013

IJCA

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This work addresses the important problem of discovery and analysis of social networks and link between frequent people observed from surveillance video footage where large amount of video data is collected routinely. A computer vision approach has been applied for detecting and extracting people within a group using HAAR classifier. This technique allows us to recognizing people by doing similarity matching between training faces and unknown detected face image. Therefore it is required to obtain high resolution face images of people in order to extract intrinsic feature information of facial images used in detection of person's faces. We present a novel frequent pattern mining based approach in the domain of frequent person detection i.e. apriori to solve frequent association problem between social networks obtained from low level task of face recognition. Our approach is illustrated with promising results from a fully integrated camera system.

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“…The construction of the RGB-HS-CbCr skin color model is described in Section 3. Section 4 presents the use of morphological operations in our algorithm [30,33]. Section 5 represents the pseudo code of the system Experimental results and discussions are provided in Section 6.…”

Section: Introductionmentioning

confidence: 99%

Face detection using skin tone segmentation

Thakur¹,

Paul²,

Mondal³

et al. 2011

2011 World Congress on Information and Communication Technologies

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Abstract---Human face detection has become a major field of interest in current research because there is no deterministic algorithm to find face(s) in a given image. Further the algorithms that exist are very much specific to the kind of images they would take as input and detect faces. The problem is to detect faces in the given, colored group photograph. In this paper, an improved segmentation algorithm for face detection in color images with multiple faces and skin tone regions is proposed. Algorithm ingeniously uses a novel skin color model, RGB-HS-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin color distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin color and background environment.

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Segmentation Algorithm for Multiple Face Detection for Color Images with Skin Tone Regions

Cited by 21 publications

References 4 publications

Face Detection in Skin-Toned Images Through Wavelet Edges and Neural Network

Face Detection in Skin-Toned Images Through Wavelet Edges and Neural Network

Link Mining: A Computer Vision and Pattern Mining Approach

Face detection using skin tone segmentation

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