Face Detection and Localization in Skin Toned Color Images Using Wavelet and Edge Detection Techniques

Lakshmi, H.C. Vijay; Kulkarni, Sudarshan Patil

doi:10.1109/artcom.2010.34

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…5a" which was rejected as a face due large variation in facial expression is correctly classified as a face by the proposed algorithm, similarly " Fig. 5b" which was falsely classified as a face by [15] due to the edges produced at identical positions, is correctly classified in this method. Classification using neural network on both R and G channels is expected to further improve the detection.…”

Section: A Training Phasementioning

confidence: 82%

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: 82%

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

Lakshmi¹,

Kulkarni²

2012

IJCEE

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“…False acceptances are noticed only when approximation images produce similar edges at identical positions. When the same algorithm was tried after converting the input image into gray scale followed by histogram equalization and edge extraction the number of false positives are found to be more [10]. The result is tabulated in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Face Detection in Skin-toned Images Using Edge Detection and Feature Extraction Using R and G Channelsthrough Wavelet Approximation

VijayLakshmi¹,

Kulkarni²

2013

IJCTE

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Face detection and localization in complex skin toned background is a highly challenging problem. In this paper, use of combination of color spaces and edge detection in red and green channels is proposed for segmenting out the skin-tone regions. Wavelet approximations are used for the extraction of prominent features of face. Experimental results are shown to yield the improved false acceptance rates (FAR) over the algorithms that either use grey scale image for segmentation and the algorithms that do not use any edge detection.

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Face Detection and Localization in Skin Toned Color Images Using Wavelet and Edge Detection Techniques

Cited by 2 publications

References 6 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

Face Detection in Skin-toned Images Using Edge Detection and Feature Extraction Using R and G Channelsthrough Wavelet Approximation

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